Data Quality - business cases for Financial Institutions. Sava Vladov, VP Business Development, Adastra BG
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1 - business cases for Financial Institutions Sava Vladov, VP Business Development, Adastra BG 15 April 2014
2 What is this presentation about? Adastra earned A Russian bank 5M EUR due to smart x-selling and communication cost management A CEE bank 2.7M EUR due to better quality of contact information and so better contact rate in collections and A Polish bank 0.7M EUR due to better performance of application and behavioral scorecards An EU bank 80M EUR due to better quality of data in stress testing 2
3 Agenda Business Cases in Financial Institutions Credit Underwriting & Provisioning Summary Adastra fast facts 3
4 DQ Business cases in Financial Institutions Credit Underwriting & Stress testing Hugely underestimated, is the key driver of success in achieving Information Management investment benefits 4
5 Credit Underwriting & Stress Testing 5
6 CEE Bank Business Impacts summary Credit Underwriting & Stress testing Contact rate improvement due to DQ 5300 clients Response rate improvement due to better targeting and householding 289 clients Final conversion improved due to combined effects of targeting and contactibility 164 clients The P/L impact of the improved conversion rate of campaigns is 5 13% of EBIT 6
7 Model based targeting Case study from Russian bank Business improvement: More accurate targeting model for x-sell/upsell campaigns Credit Underwriting & Stress testing AS IS W/O BI Models All clients are contacted by direct mail TO BE With BI Models Communication costs managed according to propensity models and client value HIGH VALUE Profit Cost Higher profits due to more intensive communication 1.3M EUR 5M EUR MEDIUM VALUE No change at all 3.7M EUR LOW VALUE - Higher profit due to eliminated nonprofitable credits Profit increase Saved comm. cost BENEFIT 7
8 Improved Contact Information Case study from CEE Bank Business improvement: Customer data consolidation (match & merge), client contact information cleansing Credit Underwriting & Stress testing 25% of customer portfolio are duplicities Incorrect evaluation of product ownership Consequences Customers are targeted for a product that already have Customers are contacted more times with the same offer IMPACT: EUR per single campaign 57% of addresses are missing or invalid 55% of phone numbers are missing or are invalid Consequences Undelivered additional cost No communication at all lost opportunity IMPACT: EUR per single campaign 8
9 Credit Underwriting & Credit Underwriting & Stress Testing 9
10 Credit Underwriting & Summary of Business Impacts Credit Underwriting & Stress testing Higher real predictive power of scorecards Identification of economically connected entities Lower back-office expenses - less work will be redone Better identification of suspicious persons, partners, companies, addresses, phone numbers More accurate warning signals Higher hit rate, lower losses and investigation costs Underwriting 10
11 Scorecards Performance Improvement Case study from Polish Bank Business improvement: Increased application and behavioural scorecard performance Credit Underwriting & Stress testing Due to the limited availability of historical data, the scorecard s performance is lower Lower scorecard performance means lower accuracy in underwriting decision making Application SC (walk-ins) Current state Improved GINI 35,5% 45% Lift** Annual risk loss 3.4M EUR 2.9M EUR Business case EUR Behavioral SCs (current clients) Current state Improved GINI 47.6% 60% 2,9 3,4 0,8 1,0 0,7 Lift** Annual risk loss 1M EUR 0.8M EUR Business case EUR Current risk loss (M EUR) Improved risk loss (M EUR) Business case (M EUR) 11
12 Fraud Reporting & Monitoring Case study from Russian Bank Business improvement: Fraud reporting & monitoring with warning signals calibrated to historical portfolio behaviour Credit Underwriting & Stress testing Prevention tools Statistical fraud scorecard Detection tools Concentrations (early delinquencies) Velocities Triggers calibrated on historical data Business impact Lower losses due to a more accurate model for identifying suspected cases of fraud Business impact Decreased losses due to early detection stopping the spread of fraud 7.1M EUR of profit Increased approval rate with the same risk cost 0.18M EUR of profit Detected fraud and stopped losses in shorter time 12
13 Credit Underwriting & Stress Testing 13
14 Better quality of contacts Case study from CEE Bank Credit Underwriting & Business improvement: Correct contact information about customers improves the efficiency of collections Stress testing 2 million (57%) active customer addresses missing Over 2.5 million (57%) personal records had invalid postal code information Nearly 1.9 million (55%) missing phone numbers from active customers Impossible to adequately identify all customers, which impacts ability to contact customer in collections 57% of missing contacts -10% in collections effectiveness 1.8M EUR p.a. in risk cost 14
15 Stress Testing Credit Underwriting & Stress Testing 15
16 of Case study from EU Bank Business improvement: Better quality of data entering the internal rating models brings the opportunity to decrease provisions Credit Underwriting & Stress testing Portfolio: Total balance: Total provisions: Consumer loans + Car loans 2 800M EUR 480M EUR attribute Fill rate Validity Impact of DQ Impact to provisions Rating, score 70% 99% Medium 29.6M EUR Delinquency bucket 97% 99% Medium 3.8M EUR Guarantor flag 100% 99% Small 0.4M EUR Committed/Uncommitted balance 100% 93% Small 6.8M EUR DTI 84% 100% Medium 5.5M EUR Origination channel 82% 99% Small 18.1M EUR Restructuring flag 85% 99% Medium 15M EUR TOTAL impact of DQ issues into provisions: 80.2M EUR 16
17 Summary Examples of Business Cases BC area Underwriting Stress testing Client contacts availability Single customer view Improved contact rate Better targeting due to householding Less comm. costs (avoid double calls) Better fraud prevention due to cleaner client information Improved collections efficiency Prediction models Better targeting thanks to PTB models Client value based communication More accurate decision making based on credit SC Improved efficiency due to segmentation models Lower provisions due to more accurate models 17
18 18 Adastra Fast Facts
19 Adastra solutions for Banking and Finance Customer Intelligence Business Intelligence Customer Retention & Loyalty Event Driven Marketing Acquisition Cross-sell Up-sell Segmentation Campaign Management MIS Dashboards KPIs and Information Intelligence Statutory Reporting IFRS Provisions Distribution Channel Management Financial & Management Consolidation Customer Insight/ Budgeting & Planning Adastra Bulgaria Ltd. Basel II Fraud Management Anti-Money Laundering Underwriting 38B Cherni vrah Blvd. Sofia 1407, Bulgaria Risk Intelligence 19
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