Risk Management of the financial Sector. Periklis Dontas Executive Director, Calculus Global Services Aybike Aker - Senior Consultant, FICO

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1 Risk Management of the financial Sector Periklis Dontas Executive Director, Calculus Global Services Aybike Aker - Senior Consultant, FICO

2 Agenda Advisory Services - The future of the financial sector Scoring - The Power of Scoring across the Lifecycle for Better Risk Decisions in the New Big Data World 2

3 Agenda Advisory Services - The future of the financial sector Scoring - The Power of Scoring across the Lifecycle for Better Risk Decisions in the New Big Data World 3

4 The European financial sector (1/4) Current environment Prospects Continuing overall recessionary pressures Liquidity / funding needs Regulatory Basel III implementation ECB: direct supervision of EU 128 major systemic banks by November

5 The European financial sector (2/4) As a result the European banking sector is undergoing major changes which will most likely further intensify: Deleveraging of non-performing loans (NPLs) Deleveraging of non-core assets Consolidation 5

6 The European financial sector (3/4) Redefining business strategies in terms of: Market, customer segments Product and distribution strategies Click call face / branch Focusing on core business deposits loans Best of breed 3 rd party business Outsourcing 6

7 The European financial sector (4/4) The main business / strategic driver will be risk management determining the optimum use of limited capital and funding. Risk management of credit processes is the main business focus of our consulting services. Our approach: Current State Assessment 7

8 Concept of the Current State Assessment The Current State Assessment (CSA) is designed to undertake a review of organisations current decisioning, analytic, technical and/or operational capabilities and its alignment to the strategic direction of your organisation. The CSA develops a prioritized roadmap of recommendations to meet up the best practices. The Current State Assessment offers a comparison on: Analytics: Data Availability, Model Development Process, Model Performance Operational and Decisioning: Decision Strategies, Decision Management Systems, Organisational Structure, Market impacts. Technical Infrastructure The Current State Assessment consists of an on-site visit by a dedicated FICO/Calculus Consulting Team (Business, Analytic, Operational and Technical) 8

9 A two-staged approach Advisory Assessment Phase 1 Conduct a Profit, and Risk Assessment to identify areas of opportunity Work with the client to develop a hierarchy to the recommendations to take the organisation to business excellence Drive out detailed roadmap for the recommendations Phase 2 Management of recommendation execution Evaluation of results Stage 1 Data Gathering Stage 2 Assessment Stage 3 Roadmap Stage 4 Execution Stage 5 Evaluation Phase 1 - CSA Phase 2 - Partnership 9

10 Agenda Advisory Services - The future of the financial sector Scoring - The Power of Scoring across the Lifecycle for Better Risk Decisions in the New Big Data World 10

11 How FICO Improves Decisions Improve decisions in specific decision areas Define and manage decision logic with business rules Target decisions based on future behavior with predictive analytics Select ideal action to maximize profit / revenue with optimization Marketing Originations Segment 1 Action 1 Customer Management Customers/ Prospects Collections/Recovery Segment 2 Action 2 Fraud Improve results across decision areas Test, learn and change through adaptive control 11

12 Analytics Across Life Cycle Identify decision area, goals, metrics Improve decisions in specific decision areas Which customer should be offered product X? When should the product be offered, at what price? Marketing Originations Customer Management Collections/Recovery Fraud Will customer leave/stop using the product within the next X months? Will the customer default within the next X months? What percent of the current balance will a customer pay back over the next six months? How to best collect from a customer? Improve results across decision areas Should this transaction be authorized? 12

13 Predictive Modeling Bal01=$450 Bal02=$624 Bal03=$328 Util1=23% Util2=31% Util3=16% TOB=86 etc etc etc etc 400+ characteristics Probability of credit default in next 12 months = 1.5% Score = 420 A Scorecard is a type of predictive model that provides an easy to understand and utilise measurement that rank orders the probability that a customer will display defined behaviour e.g. desirable payment behaviour, based on the application information. 13

14 Improve decisions in specific decision areas Analytics Across Life Cycle - MARKETING Marketing Decisions Whom to target What product to offer Channel Timing Predictions Response Revenue Risk Better Business Results Increased market share Improve results across decision areas Increased share of wallet Reduced marketing costs Increased revenue 14

15 Improve decisions in specific decision areas Analytics Across Life Cycle - ORIGINATIONS Decisions Predictions Originations Improve results across decision areas Approve/decline Line/loan/lease amount Price Risk Better Business Results Increased approvals Reduced losses Increased activation/usage Reduced origination costs Reduced application fraud Revenue Capacity Pre-payment Fraud 15

16 % Bads Detected How Better Predictions Yield Better Results Trade-off of Approval Rate vs. Bad Rate 100% Model A Model B Random Current approval rate and loss rate Reduce losses at same approval rate Increase approvals at same bad rate 0 100% % Applications Declined 16

17 Analytics Across Life Cycle CUSTOMER MNGT Improve decisions in specific decision areas Customer Management Decisions Line management Repricing/renewal Authorizations Cross-sell Pre Collections Predictions Risk Revenue Attrition Capacity Fraud Improve results across decision areas Better Business Results Increased revenue Reduced losses Reduced attrition Reduced transaction fraud 17

18 Analytics Across Life Cycle C & R Improve decisions in specific decision areas Collections/Recovery Decisions Line management Strategy Segment Contact Y/N Contact Method Contact Strategy Predictions Risk Revenue / Cash flow Attrition Capacity Self-cure Improve results across decision areas Better Business Results Increased collections, revenue Increased C&R, cash flow Reduced EAD Reduced roll rates, attrition, & provisioning Reduced charge-off, LGD Improved C&R capacity planning 18

19 Pre-Collection and Early Collection 19

20 Late Collection / Recovery 20

21 Dual Score Approach for Better C&R Strategy 21

22 Analytics Across Life Cycle Fraud Improve decisions in specific decision areas Fraud: Application / Transaction Improve results across decision areas 22

23 FICO Fraud Analytics Fraud-Trained Learn complex relations from identified fraud in historical data, then leverage this to predict fraud Outlier Method Uncover new types of fraud by identifying outlier and aberrant claims Link analysis Identity Resolution and Social Network Analysis Fraud Use tags to differentiate Learn patterns identify aberrance Connects Fraud Rings across silo-ed data by detecting and linking identity information 23

24 Types of Scorecards: Expert vs. Empirical 24

25 Account/Product/Customer Level Level 0 Decisions are taken on an account using only information available about that account Level 1 Decisions are taken on an account using information available about that account plus other accounts the customer has with the bank (both in the same product category as well as other product categories) Level 2 Decisions are taken on a customer using information available from all accounts the customer has with the bank. Decisions are executed at the account level consistent with the customer level strategy. Primary considerations are risk and profit. Level 3 Decisions are taken on a customer based on an understanding of the customer s wants and needs. Primary considerations are customer share of wallet and mid- to long-term profit. 25

26 Account Level Score Inputs Checking Account Loans/Lines of Credit $ $ Performance Outputs # # Score Score 26

27 Account Level Score (Enhanced) Inputs Checking Account Loans/Cards/Overdraft $ $ Performance Outputs # Score # Score 27

28 Inputs Checking Accounts Product Level Score Loans/Lines of Credit $ $ Performance Outputs # # Score Score 28

29 Product Level Score (Enhanced) Inputs Checking Accounts Loans/Lines of Credit $ $ Performance Outputs # # Score Score 29

30 Customer Level Score Inputs $ Outputs # Customer Performance 30

31 Customer Level Modelling Approach One product dominates performance No Have existing behaviour scorecards? Yes No Yes Need to make product decisions No Yes Yes Will portfolio of product types expand Yes No Option 1 Customer score at key product level Product outcome Other products as predictors Option 2 One score per customer Customer outcome Option 3 Multiple customer product scores Combined single customer score Customer Strategy Customer Strategy Product & Customer Strategy 31

32 FICO Model Builder Deliver Insights from Data Both Big and Small Quickly develop and deploy analytics with timesaving features and extensibility Extract business insights from your Big Data assets including very large, disjoint and unstructured data Use industry-leading FICO scorecard technology to combine analytic science and business expertise Deploy Access Prepare Design solutions using intuitive wizards, then generalize for repeated use with automatically generated scripts Model Explore Collaborate easily with reusable libraries and templates, and bolster governance with robust model documentation 32

33 FICO Model Builder: For Big Data and Big Analytics 33

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