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Analytics: Evaluating first-time defaulters from the inside out - POV
 

Analytics: Evaluating first-time defaulters from the inside out - POV

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This POV discusses how financial institutions can interact and utilize data analytics to identify and to retain first-time defaulters. ...

This POV discusses how financial institutions can interact and utilize data analytics to identify and to retain first-time defaulters.

Please download the report to learn more about how Evaluating first-time defaulters from the inside out- Intelligent segmentation helps lenders identify and target new opportunities, sheds light on the ways banks and other financial institutions can interact and utilize data analytics to identify and retain first-time defaulters in order to build profitable, long-term relationships.

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    Analytics: Evaluating first-time defaulters from the inside out - POV Analytics: Evaluating first-time defaulters from the inside out - POV Document Transcript

    • Evaluating first-time defaulters From the inside out Intelligent segmentation helps lenders identify and target new opportunitiesDeloitte Center for Financial Services
    • Contents1 Foreword2 Identifying first-time defaulters – A potentially valuable segment3 Diamonds in the rough – Using analytics to tap into opportunity6 Intelligent segmentation approach: Putting it into practice
    • Foreword Nearly six months ago, we unveiled the results of a national consumer study that identified a growing customer segment known as first-time defaulters. As the industry began to look at this new customer, banks began asking how and what could be done to address these particular customers’ needs while making them a profitable contributor to the organization’s revenues. This paper aims to shed light on ways bank can interact with first-time defaulters. In particular, it focuses on how banks and lenders can use data analytics to identify and retain these nuanced customers to build profitable, long-term relationships. Applying a predictive modeling approach to current and prospective customers can give financial institutions tools to define customer needs and risk. Like the general population of banking customers, first-time defaulters can be evaluated across the customer development lifecycle but with implied differences involving customer acquisition, customer servicing, cross- and up-selling, and custome retention. Once first-time defaulters have been identified, banks may create offers that improve short- and long-term profitability by using an approach based on collecting, formatting and manipulating data, identifying customer segments, and defining value propositions for each identified segment. Using these enhanced capabilities may allow banks and lenders to effectively target, acquire and retain liquidity-seeking first-time defaulters in a challenging market. Regards, Andrew Freeman Deron Weston Executive Director Principal Deloitte Center for Financial Services Deloitte Consulting LLP
    • Identifying first-time defaulters –A potentially valuable segment Since the most recent economic crisis, many US consumers Who are the first-time defaulters? have experienced significant financial hardship. Many • Those who had a negative credit experience, such as a Americans have found themselves without a job, behind delinquency, foreclosure, bankruptcy, and/or charge-off, on their mortgage or unable to keep up with credit card for the first time since September 2008.3 payments. Some of these individuals, previously with a good credit standing, became delinquent or defaulted on • Those who were more likely to miss their credit their debt obligations for the first time. According to a obligations as a result of macroeconomic conditions survey conducted by the Deloitte Center for Financial (such as unemployment and reduced income) than poor Services, fully 22% of Americans with bank accounts decision-making or a lack of financial discipline. experienced a serious negative credit situation during the last two years, half of them for the first time in their credit • Those with a greater propensity to seek out loans in the histories.1 future. In need of credit, first-time defaulters were more likely to obtain loan products than their prime Financial institutions and their customers appear to be counterparts, possibly making them a source of gradually recovering from the recession, and the much-needed revenue for lenders in the future. contraction in the retail credit markets appears to be easing. Lenders have begun to look for new ways to Some leading-practice banks employ various degrees of revitalize their lending businesses. Offers to riskier sophistication related to data analytics, but in general borrowers have been increasing,2 as financial institutions there are many opportunities for the industry to adopt may have realized that a larger-than-normal portion of the these practices. Specifically, if banks can identify first-time credit-challenged population may not have been reckless defaulters in their customer base, a particular opportunity borrowers, even if they did experience a negative credit exists to acquire long-term customers with favorable risk/ situation. Over time, these individuals may continue to return characteristics. For example, one large financial improve their financial standing and seek to avoid future institution is testing a targeted credit card offering, credit problems by deleveraging, limiting excess designed for customers whose credit was damaged during consumption, and increasing their savings. This is the the recession. Borrowers are required to link their credit segment we refer to as “first-time defaulters.” card account to a checking, savings, or brokerage account so that the financial institution can withdraw money from that source if a payment is missed. Meanwhile, use of the card helps the customer to rebuild his or her credit score. Also, in the third quarter of 2010, there was a significant increase from 7% of total offers in 2009 to 17% in 2010 in the number of credit card offers to previously prime customers with blemished credit.4 This share is expected to increase further during 2011. Additionally, banks reported an increased willingness to make consumer installment loans.5 As used in this document, “Deloitte” means Deloitte Services LP and Deloitte Consulting LLP, which are separate subsidiaries of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting.2
    • Diamonds in the rough –Using analytics to tap into opportunityHow can financial institutions take advantage of this How can these “diamonds in the rough” be uncovered?market opportunity? Advanced data analytics can be used For existing customers, data from traditional internalto identify, acquire, solicit, and retain “first-time sources, such as historical account activity and paymentdefaulters” who have the potential to become valuable performance, can be combined with nontraditionallong-term banking customers. Advanced analytics is the external individual or household-level data sources, suchprocess of converting a wealth of data into actionable as lifestyle data (e.g., interest in health, sports preferences,insights through statistical and mathematical models. magazine or newspaper subscriptions, type of work, etc.), retail purchase patterns (e.g., average likely market basket,Using a predictive modeling approach that focuses on eating-out spend, etc.), social media (i.e., personal datacurrent and prospective customers, internal data can be generated from social media/networks used to createsupplemented with a variety of external data sets, giving more personalized products), U.S. Census data, etc.organizations the tools to define customer needs and risk.This can be particularly effective in segmenting potential For potential new customers, banks can also make use ofcustomers who may appear to have similar characteristics. credit bureau data, looking at individual borrowingSome members of this population may in fact have specific records, the trajectory of their credit score, and thecharacteristics that help identify them as candidates to number of bureau inquiries among other metrics. Armedbecome good long-term customers with high value to the with this information, organizations can unlock neworganization. For example, among a group of apparently insights into customer populations by using analytics tosimilar 22 to 32 year olds who are in default, an individual apply a customer lifetime value model to create andwhose characteristics include a certain career field, evaluate variables, develop predictive models, and scoreeducation level, or geographic location might have the individual profiles (Exhibit 1).potential to become a valuable customer.Exhibit 1Using analytics to unlock insights into customer populations Innovative Business Modeling data sources value Traditional Nontraditional Predictive analytics Customer acquisition internal data external individual or sources household-level data sources Data aggregation and data cleansing Consumer Product Lifestyle Customer servicing mix/margin Evaluate and Financial create variables Customer Behavioral transactions Develop Cross- and up-selling Acquisition Household predictive cost/retention models Nontraditional data unlock new insights into Customer retention Score individual customer populations profilesSource: Deloitte Consulting LLP Evaluating first-time defaulters From the inside out 3
    • Customer acquisition Delivering customer service effectively improves the lifetime Data analytics can help financial organizations to move value of the customer, whether this service includes beyond traditional ‘likely to buy’ marketing models to providing a single point of contact or waiving account fees. identify customers who have a specific need. Improved customer segmentation facilitates more effective targeting As expected, customer satisfaction among individuals with and acquisition efforts. This insight can allow banks to recent credit problems is very low,7 as many banks are focus their resources on customers who offer the most trying to end or have ended their relationships with significant long-term potential to the organization. customers in this segment. However, as the economy recovers and jobs rebound, the financial situation of these When evaluating first-time defaulters for potential individuals may also begin to improve, and with it their acquisition, financial institutions can use analytics to need to have access to credit cards, home loans, mutual identify those with solid potential by leveraging data in new funds, certificates of deposit, and more. ways. For example, financial institutions can consider credit-score change and degradation in conjunction with For example, a first-time defaulter with a low credit score worsening employment indicators for a certain profession may have a desire to rebuild a positive credit history. He or in a certain geography and changes in purchase patterns she may value the opportunity to learn more about saving (e.g., journal or magazine subscription cancellations), as and budgeting, setting up automatic debit for recurring well as the specific products that experience delinquency, expenses, or signing up for electronic spending alerts. Over such as a credit card or an adjustable rate mortgage. time, as creditworthiness improves, card limits may be increased, rates lowered, and additional opportunities may After identifying those customers who present the most be presented. favorable profiles, banks can use traditional communications and direct marketing activities such as Cross- and up-selling mail, direct mail, or online promotions to attract these Once a financial institution has identified first-time potential customers more effectively. As an additional defaulters with the potential to become high-value, benefit, data analytics may be used to determine a measure long-term customers, analytics could then be used to of return on marketing investment and help banks most determine effective and profitable ways of expanding high effectively allocate their marketing budget spend. potential relationships through models that predict lifetime customer value and likelihood of attrition or potential and Early adopters can capitalize on the demand from first-time intent to buy additional products. Integrated customer defaulters who are looking for financial products, whether behavior, demographic, and attitudinal data can help banks credit cards, savings and checking accounts, or home or car to understand customer needs and make the right offers. loans, at well above average pricing (within regulatory limits6). There is little evidence in the market that lenders The recovering first-time defaulter’s specific needs may are currently targeting first-time defaulters. drive financial institutions’ account targeting and new design offerings. Once a positive credit history has been Customer servicing reestablished and the customer is on a more solid financial Data analytics may provide a deeper understanding of the footing, he or she may be looking for new car or home behavioral and financial characteristics of current and loans, IRAs, or financial instruments with higher yields. future customers. Financial institutions can now improve day-to-day management of existing accounts and address By disseminating predictive analytics results throughout the needs that are particular to first-time defaulters. Through enterprise, lenders can provide a more consistent customer predictive statistical models, financial institutions could experience across various channels and can seek to improve potentially anticipate specific needs, proactively meet those customer value. needs, and potentially improve customer retention.4
    • Customer retention one-time defaulter from the ongoing bad credit risk, suchSophisticated data analytics such as evolutionary as job history, employment industry, personal liquidity, andsegmentation solutions that account for customer product types that may have caused problems (such asdemographics, attitudes, buying patterns, etc. can help adjustable-rate mortgages). This may result in a shiftfinancial institutions to identify customers who are most towards more fundamental underwriting that considers alikely to move their accounts to other institutions. Armed number of factors in addition to a credit bureau score.with this information, financial organizations can developcustomer-specific retention tactics that are consistentwith current and expected lifetime value. For example,lenders might offer lower rates or higher credit limits tothose customers who have improved their financialstanding, or communicate additional product and serviceofferings that address the individual’s needs as hisfinancial situation improves.Although many first-time defaulters may recover andresolve the personal situations that resulted in credit issues,a subset may become repeat defaulters, making themunprofitable customers. Predictive analytics can helpenable banks to identify those customers who remain atrisk and take necessary corrective actions to help preventcharge-offs. Credit policies and models may need to beupdated with application data variables that isolate the Evaluating first-time defaulters From the inside out 5
    • Intelligent segmentation approach –Putting it into practice One approach to acquiring, cross-selling, and up-selling of wanting to buy products or open an account and are first-time defaulters uses intelligent segmentation methods within the financial institution’s risk tolerances. to identify and evaluate first-time defaulter prospects. The more effective the segmentation, the more effective the 2. Identify customer segments. Develop customer analytics may be at targeting a quality customer. Many clusters based on the preliminary risk profile along with variables can be considered for segmentation, including potential profitability and “propensity to buy” using home-loan balance, income, and situation that caused the unbiased, assumption-free analytical methods. default. After the first-time defaulters have been identified, the next step is to create offers for these prospects that can 3. Define value propositions for each identified improve the financial institution’s short- and long-term segment. Target the customer segments identified as profitability and market share. potentially profitable with customized offerings that they are likely to buy and may become profitable to the bank. This approach is based on three steps (Exhibit 2): 1. Collect, format, and manipulate data. Gather historical Several well-known banks and other financial institutions account, product and customer data, external have leveraged the benefits of analytics in identifying likely demographics, and psychographics data and evaluate as prospects for credit cards or other financial products and it relates to pre-underwriting/profitability model and offering an opportunity to add profitable long-term “propensity to buy” models. This segmentation helps to customers. confirm that prospective customers have a high likelihood Exhibit 2 Customer segmentation approach 1. Collect, format, and manipulate data 2. Identify customer segments 3. Define value propositions for each identified segment Value proposition A Segment 1 Profitability ↑ Development of Propensity to buy ↓ Historical account, clusters/segments Segment profile 1 product, and based on the Business model description A customer aggregation of data data provided and models developed. Value proposition B External Internal data may be Segment 2 demographics and enhanced with Profitability ↓ psychographics external information, Propensity to buy ↓ data which lets the Segment profile 1 expanded dataset Business model description A Profitability/value “speak” and helps model create segments based on value, Value proposition X propensity to buy, and Segment N Propensity to buy, retention/churn other factors. Profitability ↑ models Propensity to buy ↑ Segment profile 1 Test /control pilots to refine models and maximize Business model description A predictive performance Source: Deloitte Consulting LLP6
    • Example: Growing a profitable credit card market Example: International bank improves value ofA credit card issuer was trying to grow profitability in the customer contactslow-income segment in Latin America, but risk The marketing policy of a large international bank limitedmanagement challenges, such as poor collection the number of customer “contacts” that could be madeperformance and high credit losses, had inhibited results. each year. As a result, prime customers often receivedThe card issuer wanted to provide tools to its card-issuing marketing communications from the most timely productbanks to help them identify the most favorable customers. group, but not for products that were the most relevant and profitable. For example, they might receive a series ofThe card issuer developed predictive models to help credit offers during the first part of the year, instead ofcard issuers and processors improve their collection promotions targeted to the specific interests of a primeperformance. The card bank wanted to be able to identify customer, such as special rates on second homes, premiumand classify first-time defaulters based on their probability credit card offerings, or mutual funds.of reestablishing a sound financial footing or acceptingrepayment agreements, as well as to improve its collections The bank needed a way to analyze customer behavior tostrategy. determine the next desirable product offer for a specific segment based on their current situation. The bank hadPredictive classification models helped the issuer to very large amounts of data to be mined, including moreseparate first-time defaulters from chronic defaulters. than 1,000 attributes and variables for more than 9 millionSeveral scoring models were created to predict the customers.probability of a given customer moving from delinquencyto a positive credit standing. A predictive model was By using data analytics, the bank’s customers were scoredcreated that forecast the likelihood of a delinquent and assigned to “offer clusters.” More than 3 millioncustomer to accept a repayment agreement and delivered a prioritized-offer candidates were identified and submitted,decision-tree optimization tool that helps increase the and 40 cluster segments were developed.effectiveness of a collection strategy. The effectiveness ofthe issuer’s collections process rose significantly after theapplication of these models. Evaluating first-time defaulters From the inside out 7
    • Conclusion As the economic climate evolves, banks and other financial institutions have an opportunity to identify customers among a unique segment of the market known as “first-time defaulters.” Data analytics provide a valuable tool to help identify and target the individuals who offer the most likelihood of long-term potential as profitable customers, in addition to providing insight regarding the most effective products and services to offer them. By applying data analysis to existing financial and third-party data, financial institutions may be able to maximize potential and minimize risk in approaching this market segment.8
    • ContactsAndrew Freeman Omer SohailExecutive Director PrincipalDeloitte Center for Financial Services Deloitte Consulting LLP+1 212 436 4676 +1 214 840 7220aldfreeman@deloitte.com osohail@deloitte.comDeron Weston Leandro Dalle MulePrincipal Senior ManagerDeloitte Consulting LLP Deloitte Consulting LLP+1 404 631 3519 +1 617 437 3449dweston@deloitte.com ldallemule@deloitte.comEnd notes1 Deloitte Center for Financial Services, “First-Time Defaulters: An underappreciated customer segment for lenders?” February 2011.2 ”More Card Offers for Consumers with Lower Credit Scores,” credit.com, Dec. 16, 2010.3 For the purpose of this discussion, a “default” refers to one or more of the following events: three or more times late on a mortgage, three or more times late on a loan other than a mortgage, three or more times late on a credit card bill, bankruptcy, foreclosure, being contacted by a collections agency, been delinquent on child support, delinquent on taxes, delinquent on medical bills, legal judgments, or charge-offs.4 “More Card Offers for Consumers with Lower Credit Scores,” credit.com, Dec. 16, 2010.5 Senior Loan Officer Opinion Survey on Bank Lending Practices, Federal Reserve, January 2011.6 Subject to the regulations defined by the CARD Act of 2009 and the Dodd-Frank Act.7 “First-time defaulters: Changes on the horizon,” Deloitte Center for Financial Services, July 2011. Evaluating first-time defaulters From the inside out 9
    • Insights. Research. Connections.Headquartered in New York City, the Deloitte Center for Financial Services provides insight andresearch to help improve the business performance of banks, private equity, hedge funds, mutualfunds, insurance and real estate organizations operating globally. The Center helps financialinstitutions understand and address emerging opportunities in risk and information technology,regulatory compliance, growth, and cost management.The Center brings a financial services integrated view to Deloitte and its network of member firms, each of which is a legally separate andindependent entity that provide audit, consulting, financial advisory, risk management, and tax services to select clients.With access to the deep intellectual capital of 169,000 people worldwide, Deloitte serves more than one-half of the world’s largest companies, aswell as large national enterprises, public institutions, locally important clients, and successful, fast-growing global growth companies.To learn more about the Center, its projects and events, please visit us at www.deloitte.com/us/cfs.This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial,investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor shouldit be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect yourbusiness, you should consult a qualified professional advisor.Deloitte, its affiliates, and related entities shall not be responsible for any loss sustained by any person who relies on this publication.Copyright © 2011 Deloitte Development LLC. All rights reserved.Deloitte Touche Tohmatsu Limited