FINANCE DEPARTMENTS:
RETHINK TRANSACTION
MATCHING RECONCILIATION
Save up to 80% of your time by identifying and overcoming commonly overlooked challenges
FINANCE DEPARTMENTS:
RETHINK TRANSACTION
MATCHING RECONCILIATION
Save up to 80% of your time by identifying and overcoming commonly overlooked challenges
For many finance departments, several of the serious challenges in
transaction matching are often overlooked. This is largely because
the exception matching process is so time-consuming and tedious
without the proper tools. This document aims to help you identify,
overcome and master these challenges.
With a thorough review of the typical tasks and solutions related to transaction
matching, you can find better ways to both utilize your time and reduce risk. The
best practices outlined here serve as a guide to choosing the most effective tools
and processes for your own organization, regardless of your current approach.
Whether you use an in-house ERP system, Excel or manual matching, you are
certain to find some essential tips on how to take your financial reconciliations
to the next level.
This definitive guide to transaction matching reconciliation:
>> Thoroughly outlines the overall and detailed tasks related
to transaction matching reconciliation.
>> Outlines both well known and lesser known challenges,
the latter of which may be inefficiently managed.
>> Critically reviews the benefits and shortcomings
of usual solutions.
>> Provides proven approaches based on industry best practice.
>> Guides you toward choosing the best Transaction Matching process
and necessary tools to use within your own organization.
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ADRA MATCH TRANSACTION MATCHING. PAGE 2
EXECUTIVE SUMMARY
WE MAY BE “EXPERTS” – BUT SOMETIMES WE
FINANCE PEOPLE DON’T KNOW
Transaction Matching is extensively applied in every
financial department. Therefore finance staff often
think they have all the knowledge they need. But the
process is surprisingly intricate and, even worse,
often solved in unacceptable ways.
WHY UNACCEPTABLE?
The unacceptable ways of solving non-matching
transaction make it difficult to ensure the correctness of the reported figures for the Month End Close
(which transaction matching aims to improve) and
create difficulties in meeting the demands of
Governance, Risk and Compliance.
Off course this is not acceptable from a practice
standard perspective and, if investigated, could be
deemed illegal. For instance, to be compatible with
the Sarbanes Oxley Act, account reconciliation is
identified as a key internal control activity. Even if
this is not a law for all companies, many have the
same demand. Inaccuracies in this area have already
had severe business consequences and will continue
to do so.
Several investigators like Deloitte, EY, KPMG, PriceWaterhouseCoopers and more, have in many cases
found inappropriate solutions to reconciliation issues.
THE SOLUTION?
GET TO THE ROOT OF THE PROBLEM.
It is important to realize the intrinsic nature of
Transaction Matching and to see it with open eyes,
even though you and your department have extensive
experience in applying it.
threshold of complexity, to examine the complex
nature of Transaction Matching.
In order to meet your demands, you need the
proper tools to address the level of complexity of
your matching. Each chapter presents the pros and
cons of doing the work manually, or in Excel, or in
an Accounting/Enterprise Resource Planning system
(ERP), or in a purpose-built tool.
With this understanding you will be able to assess
your current level of intrinsic transaction matching,
and thus have a clear picture of which solution is
best for your needs.
A valuable solution choice matrix is presented at
the end.
This white paper is a step-by-step guide through
every matching step, and each progressive
CONTINUE ››
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ADRA MATCH TRANSACTION MATCHING. PAGE 3
CONCLUSIONS
FREE
ASSESSMENT
ACCOUNTS
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IN ALMOST ALL CASES, YOU MUST AUTOMATE.
Some companies spend 95 percent of their time
focusing on the transactions that match. This leaves
only 5 percent for the exception investigation, which
then is poorly executed, resulting in unacceptable
applied solutions.
Automation can change this: If 100 percent of the
companies described above used automated software, 5 percent of their time would be spent on
handling the transactions that match, and 15 percent
on proper investigation, solutions and reporting.
In addition, many times the non-matching items are
the result of poor quality routines, division of labor,
etc. This can also be detected and addressed within
the proper investigation process.
The remaining 80 percent of time can then be spent
on other more important and motivating tasks.
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YOU NEED A TRANSACTION MATCHING RECON­
CILIATION SOLUTION WITH THE FUNCTIONALITY
TO MEET THE COMPLEXITY OF YOUR TRANS­
ACTION MATCHING. Without such a solution,
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it remains impossible to satisfactory investigate
the exceptions with respect to Governance,
Risk and Compliance (GRC).
So which is sufficient for your demands:
paper, pen and calculator, Excel, Accounting/
ERP-systems, or a purpose-built tool? Only by
assessing the complexity of your transaction
matching will you know for sure.
In most cases, insufficient knowledge of the
intrinsic transaction matching is a reason many
companies lack the proper tools, making it
practically impossible to solve the exceptions
regardless of how much time is available.
WHAT TO DO NOW?
>> Read the full White Paper.
>> Get a FREE assessment of your
transaction matching needs by
Adra Match reconciliation experts.
>> Learn more about Adra Match
ACCOUNTS the market-leading
transaction matching tool.
>> Get a custom quote for your business
case. – Talk to a sales representative.
ADRA MATCH TRANSACTION MATCHING. PAGE 4
CONTENTS
1 TRANSACTION MATCHING RECONCILIATION
6 DEFINING TRANSACTION MATCHING
7
7
7
7
FOUR STEPS AND FOUR GENERIC METHODS The four steps – whichever method you use
The four methods Any type of data, any type of transaction
8 STEP 1: DATA COLLECTION AND CONVERTING CHALLENGES
10
10
13
14
17
STEP 2: GETTING RID OF THE HORDES OF TRANSACTIONS THAT ALREADY MATCH
The unique matching reference (which isn’t always “unique”)
Do the easy one-to-one matches first
Deal with multiple data matching challenges
The detective search matches 19 STEP 3: INVESTIGATION AND EXCEPTION MANAGEMENT
20 The exceptions to the exceptions 23 STEP 4: CHECK CORRECTNESS AND REPORTING 24 Reporting at the click of a button
25CONCLUSION
26 DON’T TAKE OUR WORD FOR IT
26 SOLUTION CHOICE MATRIX
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ADRA MATCH TRANSACTION MATCHING. PAGE 5
DEFINING
TRANSACTION MATCHING
This White Paper deals with the area of Financial
Reconciliation where the hordes of transactions from
two different sources (whether debit versus credit,
or bank account versus bank statement) must be
reconciled by matching.
The definition of transaction matching, in this context,
is the process of comparing and matching corresponding transactions between two sets of data for
a given period, which ultimately should be identical.
The process also includes investigating the exceptions, finding errors to be corrected and/or identifying
open posts to be reported.
The frequency of transaction matching can span from
daily, to weekly or monthly and include matching all
transactions on the bank statement compared to those
of the registered transactions on the bank account in
the accounting system, or debit versus credit on
a control account, etc.
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ADRA MATCH TRANSACTION MATCHING. PAGE 6
FOUR STEPS AND
FOUR GENERIC METHODS
We will describe the task, its challenges, as well as the pros and cons
of carrying out the transaction matching in four different ways.
THE FOUR STEPS – WHICHEVER METHOD YOU USE
In short, these are the four generic steps to all financial
reconciliation matching:
1 Take the two data sets that need to be reconciled. Let’s say you would like to automate your
bank reconciliation. Initially you need to download the
statement from the bank’s website for a given period,
before getting the corresponding general ledger from
your accounting system for the same period.
2 I
3 Compare the data sets, and tick off the corresponding matching transactions.
nvestigate the unmatched transactions – i.e.
the list of exceptions. During your investigation
you may actually find a way of matching some transactions, but sometimes faults are instead found in the
Accounting process. If this is the case, a correcting
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journal entry has to be made into the Accounting
system. This correcting journal entry is sometimes
done with a statement explaining the identified fault,
stemming from your investigation.
4 Report. The remaining and still unresolved
transactions after your investigation are usually
referred to as open transactions. They have to go
into a report, which is signed off.
If properly done this reconciliation matching should
first be checked and then approved (and hence
signed off) by another person, to ensure your
Segregation of Duties, to reduce your exposure to
risks of errors and fraud, and to improve your com
pliance with regulatory and practice demands. Then
this report is finalized and can be used for internal
and external controls. The reports are sometimes
used as supporting documentation in the month-end
close process.
FOUR GENERIC METHODS:
> Manually
> In Excel
> Using an accounting/ERP
(Enterprise Resource Planning) system
> Using a purpose-built software tool
such as Adra Match ACCOUNTS
ANY TYPE OF DATA, ANY TYPE OF TRANSACTION
These simple four steps apply to all kinds of transaction
matching processes. For example: Bank account versus bank statement reconciliation, cash & credit card
reconciliation, sub-ledger reconciliation, inter-company
reconciliation, debit versus credit recon­ciliation in a
control account, and so on.
ADRA MATCH TRANSACTION MATCHING. PAGE 7
STEP 1/4
DATA COLLECTION AND
CONVERTING CHALLENGES
The first challenge in automating any matching is to get
hold of the two data sets that need to be reconciled.
Let’s say you would like to automate your bank matching. Initially you need to get hold of the bank’s statement on your account for a given period, and then get
the corresponding general ledger account data for the
same period, from your accounting system.
IF DONE IN EXCEL: Depending on which source
data is to be matched, the process of getting the data
can vary significantly. But normally the easiest way is
to export data from the source system.
IF DONE MANUALLY: If transaction matching is done
by hand, you would typically have printouts from the
two sources and manually tick off the matching ones.
There are many options here, like extracting data
directly from the database using either so-called “pull”
or “push” technologies. Data could also be fetched
by transfer with an FTP service (File Transfer Protocol) or a secure FTP service. Among the more recent
techniques are XML web services (eXtended Markup
Language).
But since not all sources are easily printed in the date
order, with the columns ordered as you need, you have
a second challenge: data export. This is where many
start using Excel.
Many of us are familiar with using files and most computer systems containing data can export a selection
of data to a file. However, the lack of standards, or
the fact that the few standards that exist are far too
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complex to use, has led to a multitude of file types and
structures.
OUR RECOMMENDATION is to use a so-called text
file format for export with Comma Separated Values
(CSV), in which all data is exported with a comma
between each data field. This format is proven to provide a quick and easy way of getting data and works
smoothly in cases where there are different systems
from which data must be fetched.
ADRA MATCH TRANSACTION MATCHING. PAGE 8
STEP 1/4
The third challenge is to ensure the exported data
contains all the key elements of a transaction that
you will need, for example the transactions’, account,
date, commenting text, amount, etc.
Once all key elements are exported, data has to be
restructured so that the two data sets are easily compared. Creating this structure involves looking into the
files and identifying the key elements such as the date,
amount, reference, text and other useful elements,
and then mapping these to the same element field
for the other data set – perhaps into a new file or
database. This is commonly known as converting the
data. When this is done, your data collection challenge
is overcome.
Besides being time consuming, matching with
Excel is also error prone. Once imported, data can
be wrongly formatted (a numeric field can accidently
be formatted as a text field) or accidently lost during
reformatting due to a click of the mouse, a key stroke
in the wrong cell or a comma in the wrong position.
IF IN THE ACCOUNTING/ERP SYSTEM: Some
companies use their Accounting/ERP System for
transaction matching, which has limitations. Usually
this is because it is difficult to manage data from
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other sources, for instance from another Accounting/
ERP System used in the Company group, or another
sub ledger system. There are other limitations as well,
which we will address later on.
WITH A PURPOSE-BUILT SOFTWARE TOOL:
Adra Match ACCOUNTS has a file/data converter that
can handle literally all types of formats and structures.
This is the easiest way of getting all the different files
into a reconciliation system. Inside ACCOUNTS, during the initial set-up phase, data will be presented in
a way that is easy to format, with columns in the best
order, all of which makes it easy to compare different
key elements with each other. The next time you
have to match the same data, but for another period,
this is automated, requiring just a click of the mouse.
All data will be presented for your needs. Data is also
thoroughly checked and never lost (every imported line
receives a hidden identification, allowing full traceability including who has done what and when, regardless
of matching or correcting action).
ACCOUNTS also has advantages for larger companies with subsidiaries that use different financial
Accounting systems/ERP, as well as for inter-company
reconciliations. This because the tool can import
information from different systems, regardless of
their differing file formats.
ADRA MATCH TRANSACTION MATCHING. PAGE 9
STEP 2/4
GETTING RID OF THE HORDES
OF TRANSACTIONS THAT ALREADY MATCH
Once you’ve overcome the data collection hurdle and compiled all the data you need
in a clear, structured format so that the hordes of transactions from the two sources
can be easily compared, you are ready to start the second step of the matching
process: Ticking off everything that matches.
THE UNIQUE MATCHING REFERENCE
(WHICH ISN’T ALWAYS “UNIQUE”)
The essence of matching sets of financial data is finding the unique reference in
the first set of data that matches the same unique reference in the second data set.
This might sound relatively simple, and many matches are, but one should beware!
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ONE TO ONE – THE UNIQUE REFERENCE: In many types of financial reconciliation transactions, there is a clear and unique point of reference in the first set of
data that corresponds to the same unique reference in the second set of data. This
could be a number that appears on an invoice, purchase order, lease agreement or
similar type of financial document.
Date
Bdate
Period
28/07/2014 28/07/2014 07/2014
28/07/2014 28/07/2014 07/2014
Reference
Amount
GL9099995
R V Text
Acc. No Comment
R GL CRD 0027
1931
286.00 R PAID BACS 9099995 9043055
-266.00
9099995
The unique match here is represented by the reference
number 9099995 (highlighted in orange), which appears
in blue in the reference field of the GL account, and in
yellow in the bank statement transaction, both in the
reference and explanatory text fields.
The example shown above is from Adra Match
ACCOUNTS, a transaction matching software. As you
can see, the bank uses both the transaction date and
the booking date (“Bdate”).
ADRA MATCH TRANSACTION MATCHING. PAGE 10
STEP 2/4
ONE TO ONE – THE UNIQUE REFERENCE BY COMBINATION:
However, in certain types of reconciliations, there may not be a unique reference
number for a single, stand-alone transaction (e.g. from one of the data sets).
Here is an example:
Date
Bdate
Period
28/07/2014 28/07/2014 07/2014
When this problem arises, the solution is to combine the different elements into a
single transaction that forms a unique joint reference – or at least a reference that
is ‘unique enough’ for that transaction. Such unique references can be made up
of different combinations: a customer number combined with the amount, or an
amount combined with a lease agreement and its date, and so on. The important
thing is that the different fields in one transaction makes up for the reference.
While this might sound relatively straightforward, it can be complex in reality.
For example, sometimes the fact that the unique reference combines two elements
in a single transaction means that it is also valid for both transactions from the two
different data sets. In other words, different key elements on both transactions in
the two data set may allow the reference number to be unique enough to complete
the match.
The common factor in the solution above is that there is enough data for the unique
and common reference, in just one transaction from the first data set versus just
one other transaction in the second data set. This is what we refer to as a one-toone matching. That is: One transaction in the first data set matches another transaction in the second data set.
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Reference
Amount
28/07/2014 28/07/2014 07/2014
R V Text
R
-277.69 R GL CRD 0027
277.69
GL06001276
The two fields that make up the unique reference
here – the date and the amount – are identical in both
transactions. But is this really a unique match, or was
there a transaction a day earlier or later with the same
amount?
In this case 277.69 is an odd sum that is unlikely to
reappear. But what if it was 200.00?
Acc. No
Comment
9043055
1931
In Adra Match ACCOUNTS there is a checking rule
so that these transactions can only be automatically
matched if there is no other transaction with the same
amount within a date range in the reconciliation rule
(for instance, two bank days before and two bank days
after the actual matching date).
MANY TO ONE – FOR THE UNIQUE REFERENCE: Unfortunately, you are certain to encounter more complex transactions when the “unique enough reference”
that allows you to match transactions from the first set of data with transactions
from the second set of data are not found in one transaction. Instead, they are
found in several transactions in the first data set. For example, it could happen that
several bank transfers on the bank statement might have been combined into a
single journal entry by the financial clerk. We refer to this as many-to-one matching.
Date
Bdate
Period
Reference
28/07/2014 28/07/2014 07/2014
BACS0027
28/07/2014 28/07/2014 07/2014
BACS0027
28/07/2014 28/07/2014 07/2014
GL06001276
R V Text
R
646.90 R
8,931.92 R GL BACS0027
Amount
Note here how the two amounts from the bank account
(in yellow) match the sum on the bank statement (in
blue). According to the rules in ACCOUNTS, one would
first group transactions with the same references from
the account on the same day and then test to see
whether they may match a sum of one transaction in the
bank statement within the same period – and that there
-9,578.82
Acc. No
Comment
9043055
9043055
1931
is only one such match. In this example, the two bank
transactions add up to a balancing figure in the ledger
and all references match – this is an example of a manyto-one match.
Please note: In such matching, it is important to have
two fields that are identical, such as the date and the
reference ”BACS0027” (in orange).
ADRA MATCH TRANSACTION MATCHING. PAGE 11
STEP 2/4
ONE TO MANY – for the Unique Reference: You may also encounter situations
that are the opposite to the example above – that is, when one transaction in the
first data set can only be matched by several transactions in the second data set.
We refer to this as one-to-many matching.
Date
Bdate
Period
Reference
-1,898.00
28/07/2014 28/07/2014 07/2014
28/07/2014 28/07/2014 07/2014
RV
R
-36,056.00 R
37,954.00 R
Amount
28/07/2014 28/07/2014 07/2014
GL06001276
Text
Acc. No
BACS 23082013
9043055
Date
BACS 23082013
9043055
28/07/2014 28/07/2014 07/2014 BACS0027
37,271.32
1931
28/07/2014 28/07/2014 07/2014 BACS0027
-17,496.00
GL CRD 0027
Comment
MANY TO MANY – For the Unique Reference: The most complex type of data
matching involves matching many transactions in the first set of data with many transactions in the second data set. We call this simply many-to-many matching. In such
cases, different key elements in both transactions in the two data sets provide a
“unique enough” reference that allows your match.
Bdate
Period
Reference
Amount
28/07/2014 28/07/2014 07/2014 PL0027
In this instance, although there is no easily matched
reference, it is still possible to make a match. The two
bank statement transactions (in yellow) have several
matching fields that can be grouped. In addition to the
date, we know it is an account from which the payment
has been received.
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This grouping is then compared against the sum in the
general ledger account. Again, a check must be done
to ensure that no other match is possible, such as
two bank days earlier or later, before this is accepted,
as there is no other unique reference between the
two different sources, the bank accounts or the bank
statement transactions.
8,035.50
28/07/2014 28/07/2014 07/2014 GL06001276
-35,000.00
28/07/2014 28/07/2014 07/2014 GL06001276
7,189.18
As shown above, the general ledger account (in blue)
can be grouped by date range, reference and text fields,
and is now ready for preparation. But the matching cannot be resolved until transactions on the bank statement
(in yellow) have been grouped too. In this case, the date,
reference and ”0027” reference need to be present in
the text fields. The ACCOUNTS software can then see
that the sums are identical and create a match.
RV
R
R
R
R
R
Text
Acc. No
CHRG 0027
9043055
NI 0027
9043055
BACS 23082013 0027
9043055
GL CRD 0027
1931
GL CRD 0027
1931
Comment
Many companies use codes for cost centers, projects
and other items that can also be used as matching
and grouping criteria for automatic matching.
This is a classic example of a many-many match.
This matching is particularly challenging in a manual
system.
ADRA MATCH TRANSACTION MATCHING. PAGE 12
STEP 2/4
DO THE EASY ONE-TO-ONE
MATCHES FIRST
The easiest matches are those with clear and unique
reference numbers in the one-to-one matching category. These typically consist of one transaction and
can be matched fairly easily. The only “extra attention”
that might be required is if you have to combine elements into one single transaction to get that “unique
enough” reference.
IF DONE MANUALLY: Manual matching using pen
and paper typically allows you to match some transactions in a minute – unless the volume of transactions is large. In such cases, it is not uncommon that
productivity drops off due to the fact that it becomes
a mundane and boring task.
During manual matching, simple challenges can easily
arise. For example, one source might list the transactions starting with the earliest date, whereas the other
source might list them with the latest transaction on top.
The columns could also be in different orders, making it
trickier to reconcile.
IF DONE IN EXCEL: The difficulties that arise in
manual matching, as mentioned above, are one reason to start using Excel for this purpose. With Excel,
data can then be imported, resorted and columns
rearranged – all to produce a uniform listing of the
two data sets that makes it easier to compare and
facilitates the search for the unique match.
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By using Excel when the transactions in the two data
sources are identical or equal enough so it is easy to
spot the common identifier, all of these transactions can
be matched in a minute. But for semi-manual matching, you can match around six to seven transactions a
minute. This usually makes it a bit easier on the human
eye. The search function can also be used to help find
matches and further speed up the matching, but it
becomes a tedious process of cutting and pasting
into dialogue boxes and manual marking.
But Excel can also be error-prone. For example,
when imported, data can be wrongly formatted (e.g.
a numeric field accidentally formatted as a text field),
and when reformatting data can be accidently lost by
a click of the mouse, a keystroke in the wrong cell or
a comma in the wrong position.
For smaller businesses, because the number of
accounts to be reconciled is relatively small, it makes
sense to continue using spreadsheets. The team
required to manage the financial close process is also
relatively small (one or two finance staff), so there is
also less room for data handling errors.
However, as the number of accounts to be reconciled
grows and the team managing your monthly close
process is expanded, errors can easily find their way
into the reconciliation process. Once a business has
reached a certain size, it makes sense to move away
from Excel spreadsheets, particularly for tasks like
matching. Working at full capacity, the finance department of a large organization can easily generate up to
five hundred spreadsheets a month – so it’s easy to
see how errors and duplications can creep in.
Over the past 30 years, spreadsheets have become
the mainstay of finance departments, used for all sorts
of tasks, from reconciliation to reporting and everything in between. Despite their popularity with finance
staff, they still expose your business to massive margin
for error. Although the projected rate of error when
matching with spreadsheets is only 0.8-1.8%, for a
company with a $1m turnover it represents a risk of
$80,000-$180,000.
What’s more, the damage done can also affect public
perception and trust in your brand, leading to less
tangible but just as dangerous losses.
See http://www.eusprig.org/horror-stories.htm
ADRA MATCH TRANSACTION MATCHING. PAGE 13
STEP 2/4
IF IN THE ACCOUNTING/ERP SYSTEM: Some systems are equipped with
matching functions that enable the matching of transactions if there is a clear
unique reference on both of the data sets.
But since the Unique Reference is made up of a combination of data elements,
these systems often run into problems, even within a single transaction. They are
typically not built to manage a degree of complexity beyond simple one-to-one
matching. A few of the very large ERP systems have this type of functionality,
but this can often be complex and less than user friendly.
IF IN A PURPOSE BUILD SOFTWARE TOOL: ACCOUNTS automatically matches
one-to-one records – even if the unique reference is consists of two or more elements
together with a transaction, and even if the date is close to the proximity in time, and if
there is only one such match in the banking days close to the time of the transaction.
DEAL WITH MULTIPLE DATA
MATCHING CHALLENGES
The next topic within transaction matching is to review the one-to-many, many-to-one
and many-to-many matches. As noted earlier, such situations arise when the “unique
enough” and “common reference” is not found in one transaction, but in many.
That is, the matching process involves many transactions in the second data set
(one-to-many), many transactions in the first data set (many-to-one), in both data sets
(many-to-many), or that the unique identifier is made up of information in several fields.
Such types of transactions typically take much more effort and time to match.
come in. You need to find combinations of partial sums that eventually end up with
a match. Sometimes this can be done without spending too much time on calculations, but at times it can actually take hours to make a single match. When the work
is tedious, time consuming and the match-finding tools are limited – this is when
it becomes easily to believe that things are ”good enough”, leaving the remaining
transactions to be dealt with in incorrect ways. This is one of the situations where
compromises tend to be made concerning Governance, Risk and Compliance.
To get a match, you may also
need to bring in additional
sources such as the accounts
receivable/sales ledger or customer register. This can be helpful
if you have a payment with an
amount but no code information, yet you can get information
about which other bank account
the payment arrived from. If you
have your customer register at
hand, you may be able to spot
which customer’s bank account
it is, and hence draw an accurate
conclusion about which invoice
has been paid in the Accounts
Receivables.
The calculator is needed to add
up transactions from one source
and add up transactions from
another source, so you can see
if they match.
7
8
4
1
1
9
5
2
1
C
6
3
1
AC
x
+
=
#
M
IF DONE MANUALLY: When done manually, the matching process generally
involves a piece of paper and a calculator. Even if you have a fairly good matching
reference, you might not know how many transactions from each column need to be
added up before you get an exact match. This is where the paper and the calculator
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ADRA MATCH TRANSACTION MATCHING. PAGE 14
STEP 2/4
Usually this manual work results in several transactions being unmatched. And yet,
they could have been matched – it’s just that the process is too complex process
for manual matching. So, instead of the data being matched in this second step, it
is moved to the next third step of investigation where it will take up valuable time.
In other words, manual work is not so effective.
Sometimes faults are found, such as a previously checked-off transaction that
should be used for another match. Undoing a previously checked-off grows difficult as it can be difficult to know which other transaction it was paired with. As
these faults increase, it often becomes too difficult to continue, and some people
find that they need to start all over again.
IF DONE IN EXCEL: Sometimes described as a Swiss-army knife for finance
departments, Excel is often the tool that’s instinctively turned to for making life easier.
But is it really ideally suited for everything? To handle the one-to-many, many-to-one,
and many-to-many transactions with Excel, you need to use various functions and
sometimes do a bit of programming. There are several extreme challenges with
using Excel in these cases, one of which is the “Vlookup” function, which is less
than ideal for your reconciliation process.
Vertical lookups, or Vlookups, return searches for a value (unique identifier) in
columns of information (vertical lists) and return data associated with that value.
Vlookup is a function that is most useful when you are repeatedly retrieving the
same information from a large spreadsheet with different values, rather than
for repeated searches for different criteria (where every match is different).
The Vlookup function is usually executed by the user as follows:
=vlookup(lookup_value, table_array, col_index_num).
“Lookup_value” is the action to search for the unique identifier, for example a code.
“Table_array” identifies the table where the value resides and “Col_index_num” is
the column number where the values are located.
Excel executes Vlookup by using a binary search technique to look up data. When
searching through large amounts of data, the most efficient way for a program to
retrieve results is to begin at the middle of the listed data. The program then determines which direction the required value is likely to be in, in relation to the middle
value. It then searches the data on that side, repeating the method and narrowing
the search until the value is found.
Vlookup formula: vlookup(lookup_value, table_array, col_index_num)
www.adramatch.com
ADRA MATCH TRANSACTION MATCHING. PAGE 15
STEP 2/4
0
00.0
5=4
DO YOU SEE A PROBLEM?
Although it is commonly used in Excel to retrieve spreadsheet information, Vlookup
is nevertheless a database function. As a result, using it in Excel in this manner
causes problems. One major challenge is that accountants do not base their work
on roundings or approximates. Yet Vlookup returns only an approximate match by
default, resulting in potentially incorrect results. Many users are unaware of this.
An optional argument, [range_lookup] can then be added to the Vlookup formula to
obtain exact matches. However, this search method requires data that is arranged
in order. If the data is not organized prior to execution, inaccurate results can be
produced. Since your business relies on details and accuracy, a binary search
technique is liable to cause errors that can be catastrophic (remember
http://www.eusprig.org/horror-stories.htm).
The Vlookup function in Excel is merely one of many limitations associated with
this software. It is also part of the reason Excel spreadsheets are liable to error
rates of 0.8-1.8%. While this might sound insignificant, for a company with sales of
£1,000,000, it equates to £80,000-180,000 – no small sum. With Vlookup, Excel
can help you find the matching data in the other data set, speeding up your reconciliation process. However, it does not make matching all that much quicker, since
it involvess a complex process of finding the different elements that make up the
unique and common reference. This still requires the human eye, thought and an
exceedingly good memory to be effective.
In finance departments, we know that keeping a security-relevant chronological
record, or Audit Trail function, is of utmost importance. With so many transactions
occurring in your business every day, how will you later be sure of how a match
actu­ally was made? Unfortunately, Excel has no traceability function, which becomes
evident when you find a set of data that was previously used for a particular match,
now is better suited for a different match. How do you backtrack?
www.adramatch.com
0
= 67
= 34
000
670 78
2.0
6
00 +
0=1
12.0 7 + 34 = = 67.00 4 = 678 670 =
3
+
123.6 + 78000 23.67 + 12.0
000
1
0
0
0
0
.0
.0
70
23
0.0
= 67
0=6
= 40
2.00
345 + 78000 678
0
0=1
23.0 7 + 34 = = 67.00 4 = 678
3
123.6 + 78000 23.67 +
1
0
0
23.0 400.00 = 67.00
=
0
345 + 78000
00.0
0
0
23.0
5=4
= 67
= 34
000
670 78
2.0
6
00 +
0=1
12.0 7 + 34 = = 67.00 4 = 678
3
123.6 + 78000 23.67 +
1
0
0
23.0 400.00 = 67.00
=
0
345 + 78000
00.0
0
0
23.0
5=4
= 67
= 34
000
670 78
2.0
+
0
6
0
0=1
12.0 7 + 34 = = 67.00 4 = 678
3
123.6 + 78000 23.67 +
1
0
0
23.0 400.00 = 67.00
=
345 + 78000
0
23.0
12.000
+
123.67 670 = 345 = 40
+
0.00
23.00 + 34 = 678
78
345 = 40 000 = 67.00
0 = 12.00
0.00 12
00
00 = 6
23.00 +
3.67 +
670
34
78000
78
78
= 67.00 4 = 67
123.67
0 12
+ 34 =
2..00
2.0
2.
000
00 + 67
678
23.00 +
0=
78
345 = 40 000 = 67.00
0 = 12
0.00 12
2.0
.00
00
23.00 +
3.67 +
0=6
67
70
0
34
78000
= 67.00 4 = 678
0
12.000
+
123.67 670 = 345 = 40
+ 34 =
0.0
0
678
23.00 +
78
345 = 40 000 = 67.00
0 = 12
0.00 12
2.0
.00
000 = 67
23.00 +
3.67 +
70
34 = 67
78000
78
= 67.00
8
0
12.000
+
123.67 670 = 345 = 40
+ 34 =
0.0
0
678
23.00 +
78
345 = 40 000 = 67.00
0 = 12.00
0.00 12
0 0 = 67
00
23.00 +
3.67 +
70
7
0
34 = 67
78000
78
= 67.00
8
0
Another area of importance is Segregation of duties. Yes, Excel has read and
write protection, but cannot trace who did what and how. In short, the strength
of using spreadsheets is also part of the weakness. Their flexibility makes it
possible for us to do practically anything with the tool. A great principle, however,
is to not rely on Excel for your business processes but to look at purpose-built
tools instead.
IF IN THE ACCOUNTING/ERP SYSTEM: Some accounting systems have basic
functions for matching, but most systems start to show their limitations when the
Unique Reference includes several data elements. When the match is made over
several fields in one transaction or more, it can be difficult to do the job. But a few
of the very large ERP systems do have functions for matching even many-to-many.
The system’s lack of user-friendliness, however, is a common customer response.
IF IN A PURPOSE-BUILT SOFTWARE TOOL: While the matched transactions
above in Excel are done semi-manually, Adramatch ACCOUNTS matches transactions automatically – even if they are many-to-many matches. In contrast to
Excel, no user intervention is needed since the software is fully automatic. Should
a roll-back be required, the tool knows exactly how to do it, meaning you won’t be
forced to remember the previous matching, along with other transactions for which
you now need to find a new match.
ADRA MATCH TRANSACTION MATCHING. PAGE 16
STEP 2/4
THE DETECTIVE SEARCH MATCHES
However, in some cases the matching reference is only part of an element in a transaction. For example, on one side it could be something like “banktx-12345”, while on
the other side it could read “gl12345”, where the matching reference is “12345”.
Date
Bdate
Period
Reference
RV
R
-2,897.75 R
-55.75 R
-5,998.50 R
Amount
28/07/2014 28/07/2014 07/2014 GL06001276
28/07/2014 28/07/2014 07/2014 BACS0039
28/07/2014 28/07/2014 07/2014 BACS0039
28/07/2014 28/07/2014 07/2014 PL0039
Note here that the general ledger account transaction
(in blue) has a text ”00-39” with a dash in the middle
as well as a dash and an additional number after ”-13”.
The bank statement transactions (in yellow), however,
do not – just ”0039” without a dash and no suffix.
Knowing that this is the correct reference, this is easy
enough to see.
8,952.00
Text
GL CRD 00-39-13
Acc. No
Comment
1931
CHRG0039
9043055
NI0039
9043055
BACS 23082013 0039
9043055
But if you have hordes of transactions, you need
search criteria for all possible codes, regardless of
dashes, prefixes or suffixes, which is not simple. Again,
the ACCOUNTS solution is capable of matching here
by ’seeing through’ the inconsistency in the data.
IF DONE MANUALLY: If you do the detective work manually, it occasionally pays
off, but rarely on a consistent basis over many transactions (remember that the
examples above are easy ones).
Therefore, this is usually the threshold for what one can match manually. This is
also why many transactions that actually do have a complex match are not matched
when you do it manually.
Instead, transactions that could have been matched, transactions that are due to
different errors or rounding, and true open posts are all clumped together. And they
result in unnecessary write-offs or material misstatements. Don’t forget that the unnecessary write-offs create additional minor challenges down the line in the accounting work due to the double entry logic, with one or several small loose ends creating
errors in the precise logic of accounting. This is one reason for why finance staff
often find it difficult to trust their own reported and reconciled figures. This is one
reason why demands on Governance, Risk and Compliance are not met. Possible
matches, errors and open posts are simply grouped together and therefore treated
in a common – and often incorrect – way.
www.adramatch.com
IF DONE IN EXCEL: If you use Excel and enter different search strings, and perhaps
use programmed functions like Vlookup, you will be a more effective detective, spotting many corresponding transactions. But this usually requires entering the search
string again and again. However, in many cases, identifying the many transactions
that could make the match and making sure that the sums of these numbers adds up
to the sum you are looking for, will mostly have to be calculated separately by you.
If you later find that a better match could have been done, you have no automatic
support for reversing it, or even finding the transaction that you now have unmatched.
Many are convinced that Excel can improve transaction matching, although
programming is needed. If programming has been to approximations, it usually
becomes a trick to use the smart rules in the correct order whilst still demanding
a lot of manual work such as building up the sums.
Research reveals that despite Excel, transactions are not properly matched and
resolved. Still it is tedious and time-consuming. Still problems exist.
Date
Bdate
Period
Reference
Amount
28/07/2014 28/07/2014 07/2014 BACS0027
271.50
28/07/2014 28/07/2014 07/2014 BACS0027
-7,496.00
28/07/2014 28/07/2014 07/2014 PL0027
756.00
28/07/2014 28/07/2014 07/2014 GL06001276
10,000.00
28/07/2014 28/07/2014 07/2014 GL06001276
-3,531.50
If one manages to separate the dates above in Excel
and then search for “00-27” in Excel, there is a match
on two transactions in the general ledger account (in
blue). But the match is made by three transactions in
the bank statement where the search has to be “0027”
(e.g. similar to the first search but without a dash).
Even if programmed, Excel seldom helps by making
it easy to search for similar but different criteria in two
RV
R
R
R
R
R
Text
Acc. No
CHRG 0027
9043055
NI 0027
9043055
BACS 23082013 0027
9043055
GL CRD 00-27
1931
GL CRD 00-27
1931
Comment
data sets, or to see that the sum for the blue account
transactions matches the sum of the yellow bank
statement transactions. Instead, these usually have
to be calculated separately. Therefore you still have
to do a lot of tedious manual work in Excel to get the
job done.
ADRA MATCH TRANSACTION MATCHING. PAGE 17
STEP 2/4
IF IN THE ACCOUNTING/ERP SYSTEM: Some accounting systems have
functions for matching, but these can slows down their abilities since the Unique
Reference is made up by a combination of data elements, which we already
stated. Even in the few large ERP systems, a common response is that user
friendliness is an issue.
IF IN A PURPOSE BUILD SOFTWARE TOOL: In Adra Match ACCOUNTS
there is a rules engine.
One rule could be to search for the five digits at the end of an information block
like “banktx-12345” and see if the same five digits are present at the beginning of
another information block, such as “12345-GL”. In addition, it could check whether
the sums of transactions in the first data set, versus the other data set, plus or
minus two banking days can make a match, plus or minus a half Euro for instance.
If so, this sum can be presented to the user who can now decide whether the
deviation is a matter of rounding off by the payer or not. If the user accepts it as a
match, an accounting journal for the correction of the half Euro can be generated.
But there will not be one such set of rules. Instead, there will be many rules in
which the first set of rules identifies and makes all the simple unique matches,
and then execute the rule for the more and more complex matches.
www.adramatch.com
If there is a need to reverse a match later on, this can not only be easily done, but
with full traceability. It is also easy to go over and match the transactions that are still
unmatched.
An in-depth description of the features and benefits of our pattern-based search
functionality is outside the scope of this document. You will hopefully understand the
possibilities it brings since we have many such rules that are applied for any transaction matching. With this tool, it does away with all the matches that actually do
match, although several in complex ways, so the time can be spent on the ones that
do not match.
ADRA MATCH TRANSACTION MATCHING. PAGE 18
STEP 3/4
INVESTIGATION AND
EXCEPTION MANAGEMENT
The next step in the reconciliation process is to
investigate the exceptions – that is, the transactions
that were not matched in the previous step. Even if
there are relatively few compared to the hordes that
have already been matched, there may still be many
left if you have large volumes of transactions.
The findings of your investigation into the unmatched
transactions may take several different directions:
1) You may eventually find a match in this step, although
this may be tricky, 2) There may be a transaction that is
the result of an error, in which case a correction must
be made in the source, or 3) You might reach the conclusion that some unmatched transactions will actually
be resolved in the coming period, in which case these
transactions should be registered as “open”, before being
transferred to the next period where they will be resolved.
www.adramatch.com
In certain cases, your investigation might lead to the
conclusion that some un-matched transactions will actually be resolved in the coming period. These transactions
should then be registered as being “open” and trans­
ferred to the next period where they will be dissolved.
But some transactions are dealt with in the wrong way,
due to shortcomings in the way they are investigated.
IF DONE MANUALLY: If you are matching manually, you will usually end up with more transactions to
investigate than when using Excel or a purpose-build
matching tool.
Secondly, there might be simple exceptions that can
be identified as errors or open postings that will be
resolved in the next period.
But because the complex exceptions are too complex
to be resolved manually, they are often not treated
correctly. As a result, they are clumped together and
treated in one way – errors, written off, or rolled forward – although they probably should be resolved in
different ways. These types of transactions also create
challenges in the accounting process further down
the line, due to its strict logic of maintaining a perfect
balance between debits and credits.
ADRA MATCH TRANSACTION MATCHING. PAGE 19
STEP 3/4
IF DONE IN EXCEL: Even if you are working in Excel, far too much time is often
spent on data export, import to and restructuring, not to mention the manual and
semi-manual matching involved, according to our research. Secondly, you will also
be left with many more transactions to investigate, which together with the limited
time available, implies you won’t have the time to find all the actual matches.
As a result, too little time is usually left for this third step – investigation and exception management. If this is the case, the time you have left over for the investigation
phase is usually severely reduced. In other words, instead of finding the true cause
for the exceptions, simpler solutions are applied.
THE EXCEPTIONS TO THE EXCEPTIONS
However, even if you have the best matching engine available, there are some
transactions that should not be checked off automatically. They just have to be
investigated, since sometimes they just do not make sense or can be difficult to
determine given the information in front of you.
Ledger
Bank
Open
Date
Reconciled
Bdate
Search
Period
Reconcole
Reference
IF IN THE ACCOUNTING/ERP SYSTEM: Some accounting systems have functions for matching, but falter as the matching becomes more complex. For example,
in situations that require a unique key from two fields (where none is the date field),
or where more than two transactions are to be matched. Having support tools or
functions for the investigation phase is uncommon. While a few large ERP systems
have these functions, they often have problems related to user friendliness.
IF IN A PURPOSE BUILD SOFTWARE TOOL: As you have now understood,
our transaction matching software tool, ACCOUNTS, provides an efficient alternative.
It automatically imports, restructures and matches so many more transactions in so
little time that you will now only be presented with the few transactions that can’t be
matched, even for a complex automatic match.
www.adramatch.com
Credit
Count debit
Count credit
0.00
2
0
0.00
-2,897.00
0
1
Sum
0.35
Sum remaining
-0.35
RV
R
1,897.35 R
1,000.00 R
Amount
28/07/2014 28/07/2014 07/2014 PL0027
In their research, the Global Best Practice team at Price Waterhouse Coopers found
that many companies just roll the balance forward (Connected Thinking). Again, one
reason for this is the limited time available to conduct a thorough investigation. What
eats up all the time is matching all the transactions that already match.
Debit
2,897.35
28/07/2014 28/07/2014 07/2014 GL06001276
28/07/2014 28/07/2014 07/2014 GL06001276
The dates above are matching on these transactions,
and the sums of the blue accounting transactions
are just 0.35 away from the yellow bank statement
transactions. But there is no field data reference that
ensures this is a match. It could be a coincidence,
especially since the reference fields for the blue
transactions have two different numbers. It needs
-2,897.00
Text
Acc. No
BACS 23082013 0027
9043055
NS.SYSTELMPA012
1931
NS.SYSTELMPA014
1931
Comment
to be further investigated, particularly due to the
deviation of -0.35.
Note: This picture also includes the tabs in
ACCOUNTS allowing you to see only the open items,
the reconciled items and the simple yet advanced
search tab.
As part of your investigation, you may want to cross check information from another
source system. Or you may want to call or email a colleague with the information to
get his or her input.
As a result of these cross checks, you may have to revoke some of your previous
matches. In the process, you need to keep track of them too since at a later stage
your investigation might lead you to the understanding that one of your previous
matches has to be revised.
ADRA MATCH TRANSACTION MATCHING. PAGE 20
STEP 3/4
For example, after e-mail correspondence with a colleague, you may decide to
accept a deviation on a sum, such as the 0,35 from the example above. If so, a
correcting journal entry has to be prepared for the accounting system, explaining
why in a way that it can be understood and back-tracked if necessary.
IF DONE MANUALLY: Many of these tasks are virtually impossible to perform
manually since the work is too complex or time consuming. Sharing information with
a college can also be tricky since you not only need to write down your questions
and comments, but also structure and display the background information in an
easy-to-understand way.
If the transaction matching has been manual from the start, there is seldom enough
time to do this properly. Many times, balances are simply carried forward or several
transactions are unnecessarily written off.
IF DONE IN EXCEL: With Excel, many of these tasks are possible, but often still
complex.
This can lead to unnecessary write-offs due to both a shortage of time (e.g. the data
collection and re-structuring as well as the matching took too much time) and due to
shortcomings in Excel technology.
As stated earlier, making unnecessary write-offs can lead to additional minor
challenges down the line in the accounting process due to its double-entry logic.
In other words, you have now added several small loose ends that could potentially
cause errors due to the precise logic involved in accounting.
IF IN THE ACCOUNTING/ERP SYSTEM: Of the ERP systems that have this
functionality, exception management is often seen as difficult and complicated,
according to our customer feedback.
IF IN A PURPOSE-BUILD SOFTWARE TOOL: The ACCOUNTS software tool
was developed not only for transaction matching but for the entire reconciliation
process. As a result, you no longer need to waste time matching the transactions
that already match. Instead, you are presented with the exceptions and can focus
all your energy on these.
Firstly, there are the challenges mentioned earlier with the formulas and programming. Secondly, there is the issue of having to manually summarize transactions
from the first data set and from the second data set, since Excel is occupied with
managing the search and its formulas. That is a challenge.
So far in the process, ACCOUNTS will present you with several tools that make
data matching easier. One such overall tool is what we refer to as a reconciliation
workspace.
In order to share the mismatching information, you will have to tediously copy and
paste from Excel, together with your comments. Unfortunately, it’s not always easy to
compile your comments in such a way that your colleague can take action to resolve
the matter in question.
Here the remaining transactions from the two sources will be listed. As you select
a transaction, its detail information will be displayed, and as you tick on two, three,
transactions, etc. their sum will be displayed, making it easy for you know what total
to look for.
www.adramatch.com
ADRA MATCH TRANSACTION MATCHING. PAGE 21
STEP 3/4
You can make comments on each transaction, which will be displayed on your
reconciliation reports. This enables you to swiftly document the results of your
investigation and make sure it is easy to follow up. If you manage to find the
complex match in our tools, you can now match them off with a single click.
Sometime you either have to ask for advice, or make someone else do a more
thorough check. Using our reconciliation workspace, it is also easy to send off
an email with data that you would like someone else to check and comment.
It is also easy to take the recommended action you get in response.
Finally you are left with the open transactions that you have investigated yourself,
perhaps checked up with others and/or in other systems. Your notes on these specific transactions will automatically be a part of their documentation, making it easy
to follow up later on or enable an auditor to read and check.
If you want to make a match just to be booked in the account process – even if
there is a deviation on the sum – you can make a correcting journal entry with full
good information for traceability and audit trail.
If you state it as an Open transaction (e.g. if your research shows that it will be
resolved in the next period), there will be extensive reports on all open transactions
too, allowing for full traceability and audit trail.
Transactions sent from Adra Match Accounts
Ledger
Bank
Client: Demo Client
Debit
Credit
Count debit
Count credit
2,897.35
0.00
2
0
0.00
-2,897.00
0
1
Sum
0.35
Sum remaining
-0.35
Account Group: WhitePaper
Date
Bdate
Period
Reference
28/07/2014 28/07/2014 07/2014 PL0027
28/07/2014 28/07/2014 07/2014 GL06001276
28/07/2014 28/07/2014 07/2014 GL06001276
Account no
Number
RV
0.00 R
-1,000.00 R
1,000.00 R
Amount
Text
Acc. No
BACS 23082013 0027
9043055
Comment
2
2 897,35
9043055
1
-2 897,00
Unidentified
2
0,00
Total
Bdate
Search
Period
Reconcole
Reference
1931
28/07/2014 28/07/2014 07/2014 PL0027
NS.SYSTELMPA012
1931
28/07/2014 28/07/2014 07/2014 GL06001276
0,35
The marked information in ACCOUNTS can be easily copied into an email. Just add a written comment and
send it off, and the receiver will have all data needed to investigate.
www.adramatch.com
Date
Reconciled
NS.SYSTELMPA012
Amount
1931
Open
RV
R
1,897.35 R
1,000.00 R
Amount
28/07/2014 28/07/2014 07/2014 GL06001276
In this instance, ACCOUNTS has reported an
exception since the items do not securely match
– but could. The person contacted via e-mail has
investigated the blue accounting transactions labeled
“INS.SYSTELMPA012” – and “…14” and perhaps
checked the customer register of the owner for the
paying account “51703362”. They correspond, but
-2,897.00
Text
Acc. No
BACS 23082013 0027
9043055
NS.SYSTELMPA012
1931
NS.SYSTELMPA014
1931
Comment
the payment has been rounded with 0.35 (highlighted
in orange). By clicking “Reconcile”, these transactions
will be matched, and a correcting journal will be also
made for the 0.35.
Even though this has been matched by a user,
the audit trail will still reflect the full history of the
transaction.
ADRA MATCH TRANSACTION MATCHING. PAGE 22
STEP 4/4
CHECK CORRECTNESS
AND REPORTING
The fourth and last step in the reconciliation process
is to verify that everything corresponds correctly with
the amounts stated in your accounting system. That
is, after having made the matches, corrected the erroneous entries, identified the open transactions, do the
final balances you produced actually match the final
balances of your accounting system?
If there are differences, it could be because the period
was not locked in the accounting system and updates
have since occurred. In such cases, the new data has
to be matched, and if unlucky, one of the transactions
may have had a correction. If the balances resulting
from the matching are different, you will need to
review all the matched transactions and see if you
can spot the mistake.
If you have no difference, then you have succeeded.
Then you should produce a report of which transactions that have been matched (preferably how), and
which transactions are open. Most likely you may have
www.adramatch.com
a report with all the correcting journals that have to
be made. These reports should be signed by you,
checked and counter-signed by an approver, and
stored for internal or external review and perhaps an
audit further in time. And don’t forget to ensure that
the correcting journal entries are made.
IF DONE MANUALLY: If you are manually doing this
verification of results between your own result and
those stated by accounting, or with the help of Excel,
it normally means opening up a spreadsheet, copying
in all the transactions and balances, and then adding up the value of all the unmatched transactions for
comparison with the difference between the balances.
If the sums are not the same, most likely it is a question of mistake. These mistakes happen easily when
matching transactions manually. The most common
error is forgetting to tick off one of the transactions
in a match or overlooking a small deviation in an
amount.
Human errors can occur as the result of a distracting
phone call, a question from a colleague or a multitude
of other reasons. You might even forget to finalize the
very data field and transaction you were working with.
Hopefully you can find the little error and correct the
difference, but far too often the worst-case scenario
occurs: You have to start over again.
IF DONE EXCEL: If the matching was done in Excel,
some transactions may be accidently lost during
import, reformatting or transaction matching. This can
easily happen due to an accidental move or click of the
mouse, the comma/punctuation in the wrong placed
or a double entry. Naturally, this has to be fixed.
49 214
46
7 9 0248 2 1
3
58 484 70 00
4
0
6
83
49 942 2478 0 40
7
2 8
1 8 2 40
0
8
3 94 1 2
6
2
20 .675
2
9
7
40
6 1 40
24 27 40 0 67 6
7
49 29
7.6
4
28 66
ADRA MATCH TRANSACTION MATCHING. PAGE 23
STEP 4/4
have built-in functionality to prevent this. If there is
a conflicting sum between the accounting system
and your reconciliation, it is usually easy to track
down and resolve.
If you are using Adra Match BALANCER for your
month-end close, the report on open sums in both
sources can be directly imported from ACCOUNTS.
REPORTING AT THE
CLICK OF A BUTTON
If all the sums match between the accounting system
and your result, you need to copy and paste it into
your Excel spreadsheet with your sums, sign it and
have an approver cross-check and sign it. It will now
become both the transaction matching as well as the
supporting documentation for this reconciliation.
If this reconciliation is a part of the Month End Close,
it must be stored either as a printout in a binder or as
a file on your network. But if someone needs to check
this reconciliation or how a specific transaction was
matched, would they be able to easily understand it?
Could it be easily explained in a sudden audit?
Probably not.
www.adramatch.com
IF DONE IN THE ACCOUNTING/ERP SYSTEM:
Of the ERP systems that have transaction matching,
reconciliation and reporting of bank accounts and
simple one-to-one transactions can be effective if the
accounting balance has been updated. But managing
more complex matches, open items, and other more
complex tasks usually becomes more difficult.
IF IN A PURPOSE BUILD SOFTWARE TOOL:
Because ACCOUNTS software is part of a computerized system, it eliminates the possibilities of human
error due to missing a small sum. You simply cannot
forget not to tick off one of the transactions or make
a mistake on the amount. Our reconciliation systems
Another great part of using our reconciliation tools is
that reporting is done literally with a click of a button
– no copy, paste or additions that can create deviations due to human mistakes. All the transactions are
available in the system, together with the balances.
As a result, the reconciliation report, all open transactions and the balances with an automatic control of
the reconciliation can be printed directly or saved to a
file without any additional effort.
But the reporting is not limited to the reconciliation report, as it would be when you do this manually or in a
spreadsheet. You can also print out all the reconciled
items, lists of transactions with variances, account
balances, specific journal entries or a list and sum of
them all. Actually you can print out virtually everything
available in our systems.
ADRA MATCH TRANSACTION MATCHING. PAGE 24
CONCLUSION
> MANUAL METHOD: If the number of transactions is low and the matching is uncomplicated (one-to-one matching with clear unique references) then a manual process can be the
simplest and most cost-efficient option for a micro business (up to 10 employees). Because
there are few transactions, segregation of duties and an audit trail are probably not demanded by an auditor (the finance department may consist of only one person, so the few
difficult matches can often be resolved manually). In this situation it is also unlikely that the
company would require an audit.
> E XCEL: Excel does not have an incremental system cost. It simplifies reformatting of data
to make it easier to compare and find matches, and if unique references and one-to-one
matches are the norm in the datasets, Excel can be a good choice. However, we observe
that this holds true only if the difficult matches are few and easy to resolve and only if there
are no demands for segregation of duties or audit trail. In practice, this usually should limit
the use of Excel to micro businesses of up to 10 employees.
Even with heavy programming it remains difficult simplify and automate Excel enough. A lot
of manual work is needed for imports, for formatting and, despite the heavy programming,
even the moderately complex matches are still a challenge. Investigating unmatched transactions and/or deviations in sums, managing the exceptions, easily obtaining a correcting
journal, getting a list of open items or an overview – all of this can be cumbersome and
error-prone, even if Excel is great for searching complex criteria. Data is still easily lost by
mistake, fully reversing a previous transaction is difficult, and the demands for traceability,
segregation of duties and an audit trail are not met.
> ERP SYSTEM: If the need is just for matching bank transactions (and will not expand outside this area), if these matches are just one-to-one matches with a clear unique reference,
with few difficult matches to resolve, most of the few ERP systems with features for transaction matching are usually a recommended choice over Excel. Some of the ERP systems
with transaction matching functionality provide traceability and an audit trail, and correcting
journals can be easy. However the complaint we have heard from many customers is that
these systems are seldom user friendly and often difficult to customize (a consultant usually
has to be called in).
www.adramatch.com
A few of the very large ERP systems have full-fledged solutions for transaction matching
but, again according to feedback from our customers, they are seldom user friendly and
difficult and expensive to customize.
> PURPOSE-BUILT TOOL: If a company wants to start off by reconciling bank transactions
while remaining free to move into other reconciliation areas as credit cards, inter-company,
etc, then a purpose-built tool is highly recommended. This is also true for those who want
to have a process supporting tool that meets high levels of Governance, Risk and Compliance, if the solution should be simple and straightforward to use for any amount of transactions of any complexity, or if the solution should save time while increasing accuracy. In
these cases, a purpose-built tool can save considerable time and effort, often during a very
stressful time period, while also providing the reassurance of support and improvements
from a vendor. Any business, small (more than 50 employees) or large (more than 250
employees) would benefit from considering such a solution.
Clearly there are a great deal of issues within transaction matching reconciliation that
should be carefully considered, but rarely are. Even if we as finance professionals perform
transaction matching very regularly, and have done so for years, we seldom realize the
full complexity of the process. This document may help you to see and reflect upon your
actual needs, how they ought to be met, and what method you might consider first. The
aim of this paper is to aid you in identifying some of the biggest challenges of transaction
matching reconciliation. Now is your chance to overcome and master them.
> WHY NOT MAKE A BUSINESS CASE?
We encourage everyone to make a business case considering several of the aspects,
dimensions, requirements, demands for Governance, Risk and Compliance (GRC), time
and financial aspects involved, in order to see whether it makes sense to opt for a purposebuilt system over spreadsheets. Use the solution choice matrix at the end of this document.
ADRA MATCH TRANSACTION MATCHING. PAGE 25
DON’T TAKE OUR WORD FOR IT
TIME SAVINGS: “We use Adra Match ACCOUNTS to
reconcile our nine transactional bank accounts. It handles around 5,000 transactions, which now takes only
30-40 minutes to complete. Before we had installed
Adra Match ACCOUNTS, it took 4-5 hours of intensive,
manual matching. The difference is incredible.”
SCALABILITY: “Since the matching is all automated it
doesn’t matter how many transactions we run through
– 500 or 5,000 – it takes the same amount of time,
with the same level of accuracy.”
ACCURACY: “Whitechurch is now matching down
to the penny.”
RESOLVING THE UNRESOLVED: “We can get
to the bottom of unresolved matches more easily.”
COMPLEX OPERATIONS: “On a daily basis we
need to reconcile between 2,000 to 3,000 transactions, in more than 10 different currencies involving
more than 1,000 bank accounts.”
UP TO DATE AND IMPROVED VISIBILITY:
”The main benefit we have seen with Adra Match
ACCOUNTS is the speed. Where we used to reconcile
once a week, we can now reconcile every day, keeping
our records up-to-date and improving visibility.”
TIME SAVINGS: ”With the same amount of staff
we could do five times the number of reconciliations
in the same time, if not quicker.”
ACCURACY AND TIME SAVINGS: “By using Adra
Match ACCOUNTS the Kuoni finance team was able
to increase their transaction matching reconciliation
accuracy by 40%, cut the time spent on transaction
matching reconciliation in half, and increase overall
flexibility of the process.”
RESOLUTION OF NON-MATCHES: “We are able
to better understand causes of non-matches and fix
them more quickly.”
CONTROL: “I can customize the rules myself now,
and that is a massive benefit. Now I am in control,
not the software. It’s very liberating and much more
efficient.”
Stephan Wood, ABN AMRO
Jane Everett, Kuoni Group
Dan Gregory, Whitechurch
LINK: Turn weakness into strength
www.adramatch.com
LINK: Time is money
LINK: The mission control
ADRA MATCH TRANSACTION MATCHING. PAGE 26
TASK
m
s te
m
s te il t sy
y
s
bu
Pal
ER pos e
l
st
nu
r
ce
a
o
x
u
M
E
P
M
TASK
All transaction matching areas
+
+
-
+++
Managing Exceptions
-
-
+
+++
Bank Account versus Bank Statements
+
+
+
+++
Easy management of deviational sums
-
-
+
+++
Inter Company reconciliation
+
+
-
+++
Create correcting journal entries in one click
-
-
+
+++
Settlement Accounts reconciliation
+
+
-
+++
Gather information to e-mail off, to colleague for investigation
-
-
-
+++
Credit Card reconciliation
+
+
-
+++
Add explantory comments on transactions and matches
-
-
-
+++
Cross system reconciliation
+
+
-
+++
Time saving
-
+
+
+++
Stores reconciliation
+
+
-
+++
Managing results
-
-
+
+++
E-commerce reconciliation
+
+
-
+++
Gather all correcting posts into one journal
-
-
+ +
+++
Company Group Accounts reconciliation
+
+
-
+++
Gather all open items in both sources into one journal
-
-
+ +
+++
Currency Accounts reconciliation
+
+
-
+++
Uploading of open items into Month-End Close process and solution
-
-
+ +
+++
Interim Account reconciliation
+
+
+
+++
Time saving
-
+
+
+++
Matchability
-
+ +
+ +
+++
Overviewing all transaction matching areas
-
-
-
+++
Formatting data so it will be easy to compare, find matches and fine non mathing items
-
+ +
+
+++
Overview of transactions matching status
-
-
-
+++
Data enrichment (i.e. converting texts to internal dimensions)
-
+
-
+++
Overview of transactions matching status on all areas
-
-
-
+++
Ensuring data is not accidently lost during extracts and import
-
-
+ + + + + +
Reports on ageing items
-
-
+
+++
Ensuring data is not accidently lost in the formatting work
-
-
+ + + + + +
Time saving
-
+
+
+++
Time savings
-
+
+
+++
Governance, Risk & Compliance
-
-
+
+++
Matching the matches
-
+
+
+++
Traceability on each item and match by whom and when, etcetera
-
-
+
+++
Simple matches with unique references
+
+
+
+++
Traceability from transaction import
-
-
+
+++
Simple matches with unique enough references
-
+
+
+++
Traceability for every match
-
-
+
+++
Complex matches
- Reversability for every match
-
-
-
+++
- One to One matches
-
+
+
+++
Segregation of Duties
-
-
+
+++
- One to Many matches
-
-
-
+++
Audit trail
-
-
+
+++
- Many to Many matches
-
-
-
+++
- Who imported what
-
-
+
+++
- Same matching criteria in both sources
+
+ +
+ +
+++
- Who matched which transactions
-
-
+
+++
- Different formatting for criteria “(find and match ““0039”” with ““00-39-18””)”
-
-
-
+++
- Who accepted which deviant sums, etcetera
-
-
+
+++
- Different matching criteria in first source versus in second source
-
-
-
+++
Time saving
-
-
+
+++
- Deviations in sums (with conditions - accept +- 0.50 or similar)
-
-
-
+++
Business Size
-
-
-
+++
- Matching accuracy is important ("down to the penny")
-
-
-
+++
Micro Business (up to 10 employees)
+ +
+ +
+
+
Easily made matching rules
-
-
-
+++
Small Business (up to 50 employees)
+
+
+
+++
SOLUTION CHOICE MATRIX
s
m
s te il t sy
u
-sy
b
P
al
ER pos e
l
st
nu
r
ce
Ma
Ex
Pu
Mo
- Easy to customize matching rules as lessons are learned
-
-
-
+++
Medium Enterprizes (up to 250 employees)
-
-
-
+++
Ensuring matching sum deviations are not lost
-
-
+
+++
Large Enterprizes (above 250 employees)
-
-
-
+++
Ensuring data is not accidentaly lost during matching
-
-
+
+++
Time saving
-
+
+
+++
Time savings
-
+
+
+++
Investigation
-
+
-
+++
Simple semi-manual search functions
-
+
+
+++
Simple semi-manual filter functions
-
+
+
+++
Simple semi-manual criteria functions
-
+
+
+++
Easy reversal of previous transaction matches
-
-
+
+++
- Easy to get an un-match status of earlier matched transactions
-
-
+
+++
Easy to customize matching rules as lessons are learned
-
-
-
+++
www.adramatch.com
- NEGATIVE + POSITIVE + + GOOD + + + VERY GOOD
ADRA MATCH TRANSACTION MATCHING. PAGE 27
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Adra Match develops and markets reconciliation software for a smarter Month End Close Process. With 20 years’ experience
of providing software for the automation of the Month End Close Process, Adra Match is a market leader with 3000 customers.
Reconciliation software helps accounting and finance teams to work smarter and quicker, with better control and accuracy.
The solutions streamline and standardize accounts departments and give them an overview of the company’s balancing
process at all times.
Adra Match has an origin in Scandinavia just like many other financial software companies such as Axapta, Navision, IBS, IFS,
Jeeves and Visma and today we serve customers in more than 30 countries around the world. Among our customers are Hertz,
GE Capital, KPMG, Toyota, IKEA and BMW.
WWW.ADRAMATCH.COM