Financial Modelling vs Data Analytics FINANCIAL MODELLING (DM) Financial Modelling there is currently really only 1 system (Excel) that is sophisticated and flexible enough to cater for all the different dimensions of a 3-way financial modeling (Income Statement, Balance Sheet and Cash Flow) integrated with complex business logic and accounting. DATA ANALYTICS (DA) all forms of big data and business intelligence which enable a business to visualise, slice and dice and interrogate large amounts of data and/or write specific rules relating to that data. This can also be referred to as predictive analytics (not to be confused with financial modelling and cashflow forecasting) There are of course powerful add-ins to Some examples of the systems used in Excel like Modano that turn a simple this context are Microsoft’s Power BI, spreadsheet into a powerful content Tableau, Qlikview, Domo etc. management system Benefit – Foresight and strategic alignment to cash flows and business value drivers • By understanding the value chains of business logic from the entry and exist of financial information in a model it is possible to connect the past performance to the strategic vision and hypothesis on key management decisions. This will enable the key decision makers in the business to be across all the key financial aspects which could be impacted by their decision. Navigating the Titanic through icebergs looking out the back is not a good strategy, perhaps looking forward might avoid some nasty issues. • The impact of producing and therefore selling more brown bread is not as simple as it sounds depending on the business model ie producing onsite and holding sufficient inventory, delivering by a 3rd party and if volumes are to increase significantly staffing levels may need to adjust at certain times all impacting on costs and therefore cashflow and profits. By having a financial model which already has Benefit – Greater Hindsight and Insight and elements of predicative capability. • • • Enables a business to gain insight on what’s happening in that moment (especially if connected live like your Uber App) or hindsight based on what’s occurred historically. Answering questions like how many loaves of brown bread are sold (and at what time) across all supermarkets in the country? Which branches and bankers across the country have provided discretionary discounts on home loan/mortgage interest rates to their customers and why? the business logic and assumptions driving all these dimensions it is possible to sensitise and hypothesis a decision like this. Deciding then to change the business model can also be considered depending on management’s strategic priorities. • Similarly, the impacts and elasticity of mortgage pricing can be robustly tested in terms of the net interest margin, capital and risk returns on the balance sheet can be determined using a financial model. Securitising the mortgage book and stress testing mortgage defaults can also be considered. Shortcoming – Data processing limitations • • Whilst Excel is great for FM it cannot process large amounts of data whether historical or live in production. Many people don’t even know the existence of the XLSB (Binary) file format for Excel. This format is useful when making larger spreadsheets run more efficiently and reduce their size, but it’s never enough in today’s oversupply of large amounts of data. The next time your Excel file hangs a lot or crashes try XLSB, but it might be time to consider other options. There is also a lack of talented financial modelers. The process of forecasting and planning has existed for many decades and accountants in Finance teams have been doing this process for a long time, however it hasn’t evolved. It certainly hasn’t evolved to the level of the investment banker or other professional financial modeling firms including the Big 4. Shortcoming – Lacks Strategic Financial and Cash Flow Foresight Using the above examples typically DA systems, processes and staff with those skills will not be able to provide financial based foresight of a decision if it were made relating to the above. For example, based on the above: • • Understanding the potential financial impacts (profitability, cash flow and business valuation) and scenarios relating to changes in bread production. These changes can impact cash flow on the delivery, transport and staff costs associated with volume changes and sales of brown bread across all stores if pricing and volume changes were made at particular times. Understanding the financial impact of interest rate changes, capital allocation, return on equity and potential share price changes of mortgage discounts is equally important. If these discounts were only given for particular customers meeting • certain credit criteria and incentives for bankers were aligned to these measures (not just volume as it is in most cases) perhaps the return to shareholders wouldn’t be as significant. There is a lack of supply of DA skills across all sectors which has seen many Big 4 accounting firms build large data analytics teams and capabilities to cope with this higher demand and short supply.
