Data-Driven Financial Conduct Regulation:
the FCA’s remit, datasets and research, and
opportunities for collaboration
Dr Stefan Hunt
Head of Behavioural Economics and Data Science
Big Data Analytics for Financial Services, UCL
7th January 2016
1
Remit of the FCA
We regulate most of the UK
financial markets.
Retail:
- Savings and investments
- Consumer credit
- Mortgages
- Insurance
- …
and wholesale:
- Investment banking
- Fund management
- …
2
Number correct as at 6 January 2016. Does not include consumer credit firms with interim
permissions. “Other” firms are mainly consumer credit
Objectives and powers
Strategic
objective
Operational
objectives
Ensure that financial markets function well
Market
integrity
Consumer
Protection
Promoting
effective
competition
The FCA intervenes in markets through:
• Authorising firms and people to operate
• Policy-making: creating laws
• Supervision: check compliance
• Enforcement: prosecution and punishment
…increasingly using competition analysis
Key FCA data sets
Wholesale:
1. Financial transactions / Zen
2. EMIR (interest rates, OTC derivatives)
3. AIFMD (hedge funds)
Retail:
4. Payday lending
5. Credit card statements (~ all statements for last five years)
6. Credit bureau files
7. Personal current account micro data
8. Data from large field experiments (e.g. savings,
insurance), matched with surveys
9. Product sales data (retail products, mortgages good quality)
Firms and employees:
10.Firms’ regulatory submissions, consumer complaints etc.
11.Employees’ authorisations and records
The data ecosystem
Firm regular
& ad-hoc
submissions
Supervision,
Enforcement
etc.
Complaints &
supervisory
data
Data Audit
and ingest
Elastic high-performance
cloud storage
Machine learning &
statistical models
Credit bureaus
Surveys
ONS
Other
Social media
Visualisation
Payday lending price cap
Parliament created duty to impose cap on “high-cost shortterm credit”. Structure and level decided by FCA
Questions:
1
What happens to firms and firms’ lending decisions?
2
What options are there for consumers without access
to loans? Are they better or worse off?
Data
• Requested data using formal legal powers
• Data on payday loans in 2012-3: top 37 lenders, ~99%
market
• For 11 lenders, ~90% market, all applications, denied and
accepted, including lender credit score and revenues and costs
• Match applicants across firms and to credit bureau files
using unique identifier. 6 years of data including loan
applications, holding and balances, credit events, defaults and
credit bureau credit scores
• Dataset of vast majority of first-time loan applications,
~1.9million applicants (observe 4.6 million people, ~10% of
adult population)
7
Recreating lending decisions: credit scores
‘Good’ credit score
45o – credit score
has no explanatory
power
ROC = Receiver
Operating Characteristic
8
Recreating lending decisions: customer
level profitability
Migration matrix:
𝑚𝜏,1,1
⋮
𝐌𝜏 ≝
𝑚𝜏,𝐵,1
Decision rule:
𝒂𝝉,𝟏
𝐀𝝉 ≝ ⋮
𝟎
Loan profitability:
⋯
⋱
⋯
⋯
⋱
⋯
𝑚𝜏,1,𝐵
⋮
𝑚𝜏,𝐵,𝐵
𝟎
⋮
𝒂𝝉,𝑩
𝐌0 for Q0 (migrations to Q1),
𝐌1 for Q1 (migrations to Q2) and
𝐌𝟐+ for subsequent Qs (to following Q)
∗
𝐌𝟐+
≝ 𝐌𝟐+ 𝐀𝟐+
∗
𝐌𝟏 ≝ 𝐌𝟏 𝐀𝟐+
𝐌𝟎∗ ≝ 𝐌𝟎 𝐀𝟏
𝝅𝝉,𝟏
∀ 𝚷𝝉 ≝ ⋮
𝝉∈ 𝟎,𝟏,𝟐+
𝝅𝝉,𝑩
∗
∗ 𝟐
Customer profitability: 𝜽𝟐+,𝒃 ∶= 𝟏𝑻𝒃 𝚷𝟐+ + 𝒅𝒇𝟏𝑻𝒃 𝐌𝟐+
𝚷𝟐+ + 𝒅𝒇𝟐 𝟏𝑻𝒃 𝐌𝟐+
𝚷𝟐+ + ⋯
∗ −1
𝚯𝟐+ = 𝐈 − 𝒅𝒇𝐌2+
𝚷𝟐+
9
𝚯𝟏 = 𝚷𝟏 + 𝒅𝒇𝐌𝟏∗ 𝚯𝟐+
𝚯𝟎 = 𝚷𝟎 + 𝒅𝒇𝐌𝟎∗ 𝚯𝟏
Example: Impact on customer profitability
Expected
Customer
Lifetime
Profitability
Before Cap
After Cap
Credit score
10
Use regression discontinuity design to estimate
causal effect of payday loans
1st Stage:
2nd Stage:
Probability of getting
payday loan
Probability of missing a nonpayday payment
80%
80%
40%
60%
0%
40%
250
500
750
5.9% causal impact
of payday on
missing payments
250
Internal Credit Score
11
500
750
Causal impact of payday loan use on consumers
Change in likelihood of
exceeding overdraft limit
95% confidence
interval
• Evidence suggests
payday use worsens
financial outcomes
• Use behavioural models
to assess welfare
impacts
Months relative to first loan application
Next step: identify heterogeneous treatment effects, who is gaining and
losing, using data science methods (Athey and Imbens, 2015)
More practical examples of using research
Retail:
1. Impact of annual summaries, mobile banking and SMS
alerts in personal current accounts
2. Field experiments on information disclosures in
savings and car and home insurance
Wholesale:
3. Impact of high-frequency trading on institutional
investors
13
Data Science Roadmap
Machine-driven
compliance
Text Analytics
Mis-selling or
failure propensity
Clustering
Data
Harmonisation
Data
collection &
audit
Feature
Engineering
Predictive Analytics
Visualisation
Proactive
Regulation
Summary
• DATA: FCA collects rich transaction data + legal powers to gather
more data
• METHODS: Undertaken rigorous, ground-breaking empirical research
to inform policies. Starting to use range of data science methods
• PEOPLE: Empirical economists + data scientists
• OPEN: Open to new ideas for research + collaboration. Regularly
work with world-leading academics + aim to publish in top journals
• ACCESSIBLE: Creating high-specification secure cloud environment
facilitating off-site access
15
• REAL-WORLD RELEVANT: Research has to be immediately usable to
inform policymakers
> print(‘Thank you’)