Lending Book Predictive Analytics

Predictive Analytics For Lending Book Of BusinessIn light of stringent market condition and regulatory environment, banks are looking for new data-driven insights to grow, control cost, and a new way to manage accounts at risk.

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Many of our larger clients request digital and analytics support in the context of desired outcomes. Regardless of how the client requests services – imperatives or outcomes – KPMG’s approach accommodates a discussion and is prepared to respond.

Digital & Analytic

Banks require substantive growth rates to compensate for lost revenues to new competition and an aging population.

Banks must reduce costs to compensate for higher regulatory and credit management costs and increased stakeholder expectations. Additionally, banks must reduce costs to operate in a more automated and competitive landscape .

Banks must increase the customer experience to retain existing customers and attract new millennial and high

value customers

Banks must automate largely manual risk and regulatory processes and implement digital solutions to enable,

coordinate and monitor compliance

Banks must adapt to a new business landscape with new millennial customers, untraditional competition, an

aging population and heightened regulatory demands.

KPMG enables bank leaders to solve complex business problems with digital strategies and advanced analytics. KPMG’s D&A solution is structured according to five fundamental value drivers.



Today’s disruptive technology (i.e., big data analytics) presents a paradigm shift to solve complex business problems, and has the ability to offer enhanced digital experience to the current traditional customers and emerging millennial generation.

Progression of Analytics

What happened?

Why did it happen?

What will Happen?

How can we make it Happen?



― Elements of market drivers such as low interest rates, cost of capital, funding liquidity volatility, high credit losses, and stringent regulatory environment have adversely impacted the growth and profitability of banks’ lending book of business.

― Impending FASB CECL could increase loss reserve from 30% to 50%.

― Emerging FinTech banking, with the use of disruptive technology and looking beyond conventional risk factors (e.g.; FICO score, Agency ratings, etc.), are rapidly capturing retail & small-business market share.

― Banks are looking for new data-driven insights to grow, control cost, and a new way to manage accounts at risk.

KPMG’s commercial credit experts, who specialize in complete loan life-cycle both operationally and analytically, will decompose each process component to pinpoint targeted use cases and employ predictive intelligence to:

― Enable P&L owners and RM with acquisition analytics to grow and manage origination pipeline

― Enable risk and portfolio managers with credit performance analytics to effectively manage or hedge credit quality

― Enable SAD and loss mitigation managers with collection & recovery analytics to manage or hedge SAD/REO book and control losses

An illustrative view of commercial loan life-cycle



Problem Statement:

― Elements of market drivers such as low

interest rates, cost of capital, funding liquidity

volatility, high credit losses, and stringent

regulatory environment have adversely

impacted the growth and profitability of banks’

lending book of business.

― Consume credit line of business, residential

mortgage in particular, witnessed steep losses

after the financial crises, and the rebound has

been relatively slow.

― Emerging FinTech banking, with the use of

disruptive technology and looking beyond

conventional risk factors, are rapidly capturing

retail & small-business market share.

― Banks are looking for new data-driven insights

to grow, control cost, and a new way to

manage accounts at risk.

How we can help?KPMG’s consumer credit experts, who specialize

in complete loan life-cycle both operationally and

analytically, will decompose each process

component to pinpoint targeted use cases and

employ predictive intelligence to:

― Enable P&L owners and RM with acquisition

analytics to grow and manage origination

pipeline

― Enable risk and portfolio managers with credit

performance analytics to effectively manage

credit quality

― Enable loss mitigation managers with

collection & recovery analytics to control

losses

An illustrative view of Consumer loan life-cycle

Fraud

Legend

Parties

Borrower

Co-Borrower

Agent/Officer

Exposure

Portfolio

Risk Exposure

EAD

Expected Loss

Risk Rating

Portfolio

Risk Rating

PD/LGD

Mortgage

Account

Fraud

Attributes

Mortgage

Account

Fraud

Profile

Collateral

Valuation

Event

Fraud DB, MSP, Mortgage C/O SS

Financial

Account

Repurchase

Mortgage

Account

Repurchase

Attributes

Collateral

Valuation

Event

Mortgage

Account

Repurchase

Profile

Collateral

Valuation

Event

Repurchase DB, Fraud DB, MSP

Collection

Mortgage

Account

Collection

Attributes

Mortgage

Account

Collection

Event

MSP and other collection sources

Scorecard

Broker

Attributes

Scorecard

Profile

Scorecard Application

QC/QA

Mortgage

Account

QC/QA

Attributes

Mortgage

Account

QC/QA

Profile

QC/QA Database

Recovery

Recovery Sources

Mortgage

Account

Recovery

Attributes

Mortgage

Account

Recovery

Profile

Mortgage

Account

Recovery

Event

Mortgage

Account

Credit

Application

Mortgage

Account

Mortgage

Account

Term Profile

Mortgage

Account to

Interest

Rate

Mortgage

Account

Balance

Profile

Mortgage

Account

Term

Structure

Collateral

Real

Property

Core Portfolio Structure

Ref. Data

Reference Data

Income

Revenue,

Interest,

Fees and

Pricing

Loss Mitigation

Mortgage

Account

Loss

Mitigation

Attributes

Mortgage

Account

Loss

Mitigation

Profile

Collateral

Valuation

Event

MSP, Loss Mitigation Resources

Default

Mortgage

Account Default

Attributes

Mortgage

Account Default

Profile

MSP, Repurchase DB, Mortgage C/O SS

Proposed Source Systems

Delinquency

Mortgage

Account

Delinquency

Profile

MSP and other servicing sources

Real Estate Owned

Real

Estate

Owned

Item

Attributes

Real

Estate

Owned

Offer

Event

Real

Estate

Owned

Write

Down

Event

RES.NET, MSP and REO SS

Real

Estate

Owned

Expense

Event

Collateral

Valuation

Event

Slowly

Changing

Dynamic/

Profile

Transaction

s/

Events

Core

Portfolio

Structure

CDC



Lending Book Predictive Analytics Must be Business DrivenA combination of bank’s existing data and industry’s emerging data encapsulates information value of multidimensional propensity that can, with the help of advanced technology, unleash anunparalleled predictive power to catalyze a bank’s ability to achieve growth profitably, manage credit performance optimally, and control cost by managing credit losses effectively.

— Achieve longitudinal and latitudinal growth — Expand into emerging FinTech segments — Design new channel and product offering to tap into millennial consumers — Acquire portfolio through strategic merger and acquisition— Optimize deposit/FTP margin spread, generate fee, and control customer & balance attrition

— Optimize credit performance to retain lifetime economic value of loans — Target top P&L movers to optimize risk-return goals and improve credit quality— Prevent large prepayment shift with optimal loan restructuring— Detect borrowers at risk to minimize credit cost by understanding predicted vs. actual KRIs — Control default rate/charge-offs to optimize loan loss reserve and unleash trapped capital

— Detect early signs of problem loans and accounts to optimize loss mitigation strategy — Optimize time to recovery and reduce recovery cost— Optimize collection strategy and reduce resources for collection and cost— Prevent impaired real estate loans leading to foreclosure and reduce REO book inventory — Improve SAD book asset disposition rate and increase return on SAD asset

External Industry

Data

Credit Bureau Data

Emerging Social

Media & News

Media Data

Internal Portfolio & Customer

Data

— Information Retrieval — Natural Language Processing— Network Theory, Signal Processing, and Diffusion Model— Machine Learning and Decision Science— Behavioral Science and Propensity Modelling — Knowledge Representation and Reasoning— Hypothesis and Evidence Scoring



― Identifying Potential Growth Areas― M&A Due-diligence (valuation, elasticity, churn)― Proactive targeting of loan commitment completion― Making offers and customer communication

relevant and timely― Targeting ‘risk of attrition’ customers with save

strategies― Identifying price-sensitive customers for retention

strategies― Predict profitability (i.e., RAROC) and optimal

pricing decision

― Ability to identify loans at risk of default and employ strategies for loss mitigation

― Identifying downward migration of risk rating for potential loan restructuring

― Proactive strategies on legally binding exposures for loss mitigation

― Payment adjustments to prevent risky loans becoming delinquent

― Optimized TDR and SAD disposition― Optimized allocation of collection resources― Reduced collection cost― Increased recovery― Reduces delinquency― Enhanced collection efficiency

Lending book predictive analytics use case

— Emerging Sector/New Footprint

— M&A Analytics

— Total Borrower/Relationship Exposure

— Account Renewal

— Limit Enhancements

— 360 Customer view and Customer Lifetime Value— Cross-sell/Upsell

— High Value Customers

— FinTech for Small Business Banking

— Omni channel design

— EWI and Covenant Breach

— Propensity of Delinquency

— Rating Migration

— Non-accrual Likelihood

— Time to Delinquency and Default

— Bankruptcy Likelihood

— Drawdown (speed and amount) Likelihood in case of default

— Gross and Net Charge Offs

— Obligation Pay-offs

— Line Utilization

— Deep-dive Analytics for Top 20 Accounts

— Potential to Repay— Change in Ability to Repay— Cure Rate— Recovery Rate— Time to Recovery— Return on Recovery— Likelihood of Recovery from Bankruptcy— SAD Restructuring Likelihood and Performance

— Customer Segmentation

— Campaign Analytics

— Advanced Credit Approval Scorecards

— Credit Card Activation Rate

— Credit Card Risk Profile and Limit Enhancements

— Credit Card Dormancy and Re-activation Analytics

— Balance and Account Attrition

— Cross-sell/Upsell/Next Best Product Offer

— Price Elasticity and Propensity to Buy

— Millennial consumer acquisition strategy

— FinTech for Consumer Banking

— First Payment Default— Never-Pay Analytics (Credit Card) — Propensity of Delinquency— Roll-rate Prediction— Time to Default— Gross and Net Charge offs— Foreclosure Likelihood— Credit Card High Usage— Prepay Propensity— Time to Prepay and Amount

— Potential to Repay— Change in Ability to Repay— Potential to Self-cure— Short-term default outlook (3-6 month) — Prioritizing Accounts or Segments Outreach— Agent (pricing and call volume)— Call Analytics— REO book disposition analytics



Target potential new customers at various risk profile levels to maximize return potentialAcquiring new customers, based on customer-specific risk profiles

Purpose: to identify potential new

customers, then assess their

likelihood to join USAA and the

expected return

Purpose: to determine acceptable

levels of risk/return tradeoffs, and the

cost associated with acquisition; place

potential new customers into

segments that will be targeted the

same

Purpose: to achieve a targeted

acquisition approach for potential new

customers that maximizes expected

return at lowest cost with a balance

across the risk spectrum

Segment

Risk/R

eturn

Marketing

campaign

S1 L/L Secure Checking

S2 L/M Auto Insurance

S3 M/MPreferred Cash

Awards Visa

S4 H/H VA Loan

Identification & assessment

Network Modeling

Risk Profile

Propensity to Buy

Potential Lifetime Value

Marketing optimization

— “How do I effectively target

different segments of potential

new customers?

— What campaigns do I run?”

Risk management

risk

retu

rn

Metric Score

Risk M

Return M

Cost to Acq. $K

Prop to Buy 42%

LV $2.7M



Expand product market share from existing customers through target marketing

Purpose: to differentiate banks’

customers across multiple dimensions

that impact pricing decisions

Purpose: to rollup customers into

“Pricing Segments” by grouping “like”

customers that ultimately will be

priced/marketed-to the same

Purpose: to maximize growth while

minimizing cost and managing for

risk; growth/cost/risk are all

trade-offs, where do I get the most

return on investment?

Segment Auto loan Checking

S1 Rate (S1, A1) Rate (S1, C1)




360° View of customer

Customer Lifetime Value

Attrition

Risk Profile

Rate Sensitivity

Pricing optimization

Where do I spend dollars and

accept varying levels of risk to get

biggest bang for my buck?

customer segmentation

Combining predictive modeling with optimization will enable banks to grow in a cost effectively manner while managing risk.

S1 S2

S3S4

Use existing customers base for target marketing and pricing



Optimizing product pricing through risk profiling and customer-centered ROI What is Pricing Optimization?

Pricing Optimization applying analytics to predict how customer behavior can change under different rate pricing scenarios at the

micro-market level, then optimally price products to maximize revenue, while adhering to defined business rules and constraints.

Our Approach

KPMG built a closed-loop pricing optimization solution that combines predictive modeling and optimization. The solution optimally

assigns rate to maximize balance growth and minimize interest expense, while not adversely impacting attrition and CLV.

The solution is automated and includes an iterative feedback loop to continuously improve pricing and result in higher and higher

return for the bank.

Results

Since implementation in November 2015, the bank has seen growth of 5%, reduced interest expense by 8%, and maintained

attrition.Client-Centric Pricing Optimization Solution

Model Inputs

Internal Data: Customer,

Product, Markets

External Data: Customer

Potential (IXI) and Competitor

Pricing (Informa)

Predictive Models: Customer

Lifetime Value (CLV), Rate

Sensitivity, Balance Growth,

and Attrition

Customer Segments

Pricing Business Rules

Model Outputs

Client-Centric

Pricing

Optimization Model

Segment

Product P

Tier 1 Tier 2 Tier N

S1 S1T1 S1T2 S1TN

S2 S2T1 S2T2 S2TN

SN SNT1 SNT2 SNTN

Implement prices, gather data on customer response, provide feedback to models and clusters, continuously improve pricing to maximize returns

What-if Scenario Analysis

Product Profitability

#BPS

# Exception Codes

Rate Sensitivity

CLV

# Products per Customer

Product Profitability



― Classes of data stemming from banks internal activities, internal activities, digital interaction, news media, credit bureau, and capital market information ingested into an analytical data lake using Big-Data technology. Data is mined to determine abstract patterns.

― A range of data science techniques are employed to formulate mathematical algorithm that would analyze current and historical facts to make predictions about future or otherwise unknown events.

― A set of advanced credit profile are developed using data science that is predicated upon identifying plausible relationship hidden in data ( i.e., data association) looking beyond conventional credit scoring mechanism.

― Models will capture relationships among many factors to allow assessment of risk potentially associated with a particular set of conditions, guiding decision making for candidate credit transactions.

― Mathematical algorithms are built to self-learn the probability of credit events and subsequently, determine the velocity of the events to culminate in default, enabling credit managers with early detection of problem accounts.

Predictive analytics ecosystem

CUSTOMER ACCOUNT DATA LOAN ACCOUNTING DATA

CREDIT BUREAU/AGENCY DATA

CRMLOAN/ACCOUNTORIGINATION DATA

MARKETING & CAMPAIGNDATABASES

ONLINE ADDS & COMMERCIALS

CONSUMER FEEDBACK

STOCK MARKET DATA

MOBILE BANKING STREAMS

FINTECH DATA

CAPITAL MARKET DATA

SOCIAL MEDIADATA

REGULATORY DISCLOSURES

NEWS FEEDS

Data Mining

Data Relation &

Association

Diffusion Model & Signal

Processing

Natural Language Processing

Machine Learning

Propensity Modeling

Lending book data visualization over loan life-cycle



Commercial lending book executive dashboard

— Create Credit Package

— Credit Approval

— Closing Preparation

— Document Preparation

— Closing

— Funding & Booking

— Monitor Portfolio

— Manage Exposures

— Monitor & Manage Credit Quality

— Manage Payments & Advances

— Manage Exceptions

— Manage Payouts & Release Collateral

— New Business

— Increased Exposures

— Renewals

— Underwriting Policy Exceptions

— Exposure/Outstanding

— Criticized/Classified

— NPLs, ORE, NPA

— Charge-offs

— Delinquencies

— Rating Migration, WARR

— SAD Managed

— Total Borrower Exposure Relationship

— Top Customers/Exposure & Changes

— Trading Exposure

— Top Criticized, Classified, NPAs & Changes to each

— Real Estate Collateral Concentration

— Document Exceptions

— Large Payoffs

— Pending Maturity

— Active loans with expired maturity date

— ACH Penetration

— Exceptions: Stale/Missing/Mismatch Ratings

— Weekly Credit Production Report

— New and Increased Loan Commitments

— Renewed Facilities

— Trackable Policy Exceptions (New and Renewed)

— Group/Region Line of Business Asset Quality Summaries

— Largest CRS Relationships (Exposure & Outstanding)

— NPL and ORE

— Criticized and Classified Exposures

— Risk Rating Migration

— Delinquencies (30-89, 90+)

— Credit Relationship Groups Report

— Daily Trading Exposure (Counterparty Limit) Reports

— Industry Concentrations

— Top Lists

— Past Due Reports

— Maturing Loans

— Payoffs

— Past Maturity

— Data Quality Scorecard

— Data Exceptions

— Document Exceptions

— Unassigned Loans

— Credit Quality Dashboard

— Interactive Credit Quality Reports –Customizable credit measures and time periods

— Rating Migration Report

— Customer Search – Total Borrower Exposure

— Interactive Detail Reports – Top Lists & Changes for a selected credit measure



are designed to reflect the key business drivers, metrics and risk budget components impacting organizational decision making at the executive level.

are the first layer of drill-down into specific portfolios bringing tabular and graphical data together around key performance and risk indicators, as well as risk budget components to focus attention on relevant issues.

are designed to facilitate analysis into more granular levels to better diagnose and evaluate the drivers of trends.

Consumer lending book executive dashboard

Executive Dashboard Rolling 9-12 Quarters Credit Quality SummaryCredit Performance Summary & U.S. Heat MapRepurchase SummaryFraud SummaryOrigination Quality Summary Post/Pre-FundingIndustry comparison & External Indicator Summary

Run-on and Run-offsDelinquency & Roll rate ReportingNon-Performing Assets ReportingNon-AccrualsREO90+ to 120+ DPDTDRCharge-Off ReportingFFIEC Write-downsIndustry Comparison

Production & Servicing:Units, $Amount By Geo, Property Type, Loan Type, Loan Purpose, Occupancy Type, Doc Type, LTV/FICO/DTI/DpD/Interest Rate/Maturity Term/UPB/Reg. Classification bucketsPortfolio Stratification with US Geo Map

Weighted Average SpreadsLTVFICODTIInterest Rate

Vintage TrendsDelinquencyNon-AccrualCharge-offsLoss

Advanced analytics & digital finance management (FinTech landscape)



Leveraging Big Data Analytics, Artificial Intelligence and Machine Learning Across FinTech

Analytical rigor in FinTech landscape

Credit scoring

Marketing

Risk management Investment management

Customer

acquisition

Customer

retention and

loyalty

— Gather customer data from

multiple available sources

— Quantify qualitative aspects

— Customize scoring models

iteratively

— Customer acquisition: Focus on

digital channels

— Improvising digital touch points

to engage consumers

— Creating complete customer

preference profiles by going

beyond transactional data

— Personalized, contextual

offerings

— Enhanced fraud & authentication

solutions

— Eradicate vulnerable access

points

— Device identification, biometrics,

behavior analysis

— Automated advisory solutions

— Combine multiple data points

(social media, search data, etc.)

and provide visual insights

— Identifying anomalies

Note: Company list is not exhaustive and is focused on startups

Source: LTP, Powered by MEDICI

Thank you

ABCD

Saroj Das

Managing Director

Credit Risk Consulting

303 Peachtree Street Tel. +1 703 343 2336

Atlanta, GA 30308 Mobile +1 404 242 6852

[email protected]

Lending Book Predictive Analytics

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