big data, voice analytics and unstructured data help tackle the compliance conundrum
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Big Data, Voice Analytics and Unstructured Data Help Tackle the Compliance Conundrum
Omer SohailLloyd WirshbaDeloitte Consulting LLP
October 17, 2013
The CFPB is a data driven regulator whose rulemaking authority influences the entire consumer finance spectrum
Source (L to R), accessed September 12-17, 2013, web articles
http://www.americanbanker.com/issues/178_176/customers-are-now-banks-greatest-regulatory-threat-1061975-1.htmlhttp://www.americanbanker.com/issues/178_164/lenders-press-cfpb-to-delay-qualified-mortgage-regs-start-date-1061547-1.html http://www.americanbanker.com/issues/178_131/cfpb-to-debt-collectors-were-watching-you-1060478-1.html
The CFPB Complaint Database provides perspective on industry trends and issues
Source: CFPB complaint database, accessed August 28, 2013
• Contains over 130,000 complaints across seven products, where the date of first data collection varies by product
• Mortgage complaints represent both the majority and an increasing trend: 53% overall, from 48% in March 2012 to 63% in March 2013
• The timely response rate is 97.6% (acknowledgement within 15 days)
• Consumers disputed 21.2% of proposed resolutions, while 12.6% are still awaiting response
20278; 15% 3769; 3%
24850; 18%
10012; 7%192; 0%
72436; 53%
4528; 3%
CFPB Complaint Database by Product
Bank account or service (March 2012)Consumer loan (March 2012)Credit card (November 2011)Credit reporting (October 2012)Money transfers (April 2013)Mortgage (December 2011)Student loan (March 2012)
Finding complaint root causes requires identifying predictive indicators within other data sources
Source: CFPB complaint database, accessed August 28, 2013
• Nearly 85% of mortgage complaints relate to loan mods and servicing
• During origination, consumers find another lender instead of complaining
• Consumer awareness is increasing• The mortgage servicing arm of a large
bank sought to identify the root cause of an increase in mortgage complaints
• Each complainant that could be connected to internal records contacted the bank at least three times prior to the CFPB complaint
The CFPB database has no predictive indicators, so it must be joined with other sources to analyze and respond to CFPB complaints
2011 / 12
2012 / 2
2012 / 4
2012 / 6
2012 / 8
2012 / 10
2012 / 12
2013 / 2
2013 / 4
2013 / 60
1000
2000
3000
4000
5000
6000
Mortgage Complaint Issues over Time
Loan modification,collection,foreclosureLoan servicing, payments, escrow accountApplication, originator, mortgage brokerSettlement process and costsOtherCredit decision / underwriting
Com
plai
nts
The CFPB taxonomy provides more issue types for credit card complaints
Source: CFPB complaint database, accessed March 31, 2013 Nilson Report, February 2012 (outstandings at end of 2011)
• The top five of 33 issues constitute 46% of the 24,850 credit card complaints
• Integrate data from complaint handling, process improvement initiatives and product offerings to train a system that suggests a bank-specific root cause
Product Num. Issues
Bank Account/Service 5
Consumer Loan 7
Credit Reporting 5
Money Transfers 6
Student Loan 3
16%
10%
7%
7%
7%6%
48%
Credit Card Complaint Issues
Billing disputesAPR or interest rateCredit reportingIdentity theft / Fraud / EmbezzlementClosing/Cancelling accountOtherNamed other issues (27 categories)
Providing relief is not a primary tactic to reduce consumer disputes
• Despite resolutions in favor of the consumer decreasing, consumer disputes also decreased
(Originally the CFPB only had one category of relief. In May 2012, the options were expanded to monetary and non-monetary relief)
• Better communication with customers may help firms manage consumer expectations and support process improvement
Source: CFPB complaint database, accessed August 28, 2013
2011 / 11
2012 / 1
2012 / 3
2012 / 5
2012 / 7
2012 / 9
2012 / 11
2013 / 1
2013 / 3
2013 / 5
2013 / 70%
5%
10%
15%
20%
25%
30%
35%
Complaint Resolution Statistics
DisputesResponses in favor of consumer
Providing relief has a different effect on dispute rates depending on the product
• For consumer loans and mortgages, providing relief reduces the dispute rate by 7%
• For other products in the original database, providing relief reduces the dispute rate by 13%
• To better allocate resources while reducing complaint volume, it is critical to understand the relationship between providing relief and customer satisfaction. Which opportunity is greater?
Source: CFPB complaint database, accessed August 28, 2013
Consumer
loan
Mortgag
e
Bank a
ccount o
r serv
ice
Credit c
ard
Studen
t loan
0%
5%
10%
15%
20%
25%
30%
Dispute Rate by Product and Response
Closed with relief Closed without relief
Disp
ute
Rate
For bank account and service complaints, disputes are greatly reduced with fee refunds
• Nearly a quarter of bank account and service complaints resolved without relief were disputed
• Compared to providing non monetary relief, providing monetary relief reduces dispute rates from 18% to 9%
• Does the refund amount relative to the fee(s) responsible for the complaint impact the dispute rate?
Source: CFPB complaint database, accessed August 28, 2013 Pennsylvania PIRG CFPB Complaint Database analysis, accessed September 19, 2013 http://www.pennpirg.org/sites/pirg/files/reports/Big%20Banks%2C%20Big%20Complaints%20screen%20vPA.pdf
Closed (7
58)
Closed w
ith ex
planati
on (10550)
Closed w
ith m
onetary
relief
(4473)
Closed w
ith non-m
onetary
relief
(1070)
Closed w
ith re
lief (1
220)
Closed w
ithout r
elief
(1990)0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
66% 65%80%
72%86%
74%
21% 24%9%
18%
14%26%
12% 11% 11% 10%0% 0%
Bank Account or Service Complaint Disputes
Pending ResponseDisputedNot Disputed
Complaints are more likely to originate from zip codes with older and more affluent residents
• Adjust for penetration, separating out each banking product
• Complainants in the database may not precisely represent the customer population at a financial institution
• Banks should understand their exposure, especially which involves consumer segments that are important to the CFPB: underbanked, lower income, or other disadvantaged populations
Source: CFPB complaint database, accessed August 28, 2013
Population Complaints0%
10%20%30%40%50%60%70%80%90%
100%
Mortgage Complaints by Median Age
D (>= 41)C (37.5-40.9)B (33.5-37.4)A (<33.5)
Population Complaints0%
10%20%30%40%50%60%70%80%90%
100%
Mortgage Complaints by Median Income
H (>= $69,000)G ($52,000-$68,999)F ($41,000-$51,999)E (<$41,000)
1.17
1.05
1.00
0.75
1.43
1.08
0.84
0.62 Values > 1 indicate a larger share of complaints from that age/income group than is represented in the population
Deriving value from complaint data analysis
Components of an enterprise-wide complaint analytics capability
Social Media
News
Call Transcripts and Agent Notes
Focus Groups
Online Chat
With structured data, collect unstructured and external data
Integrate with a cross-functional complaint management program
Data and Analytics
Governance and Controls
Escalation Process
Employee Training
Regulatory Reporting
Independent Compliance Audit
Perform historical reporting and advanced data analytics
Data aggregation and cleansing
Create relevant variables
Develop predictive models
Develop reason codes and business rules
Refine modeling outcomes
Building and deploying leading analytics requires a combination of domain, data management, data intuition, statistics and technology skills
Complaint analytics drive value across all bank functions
Operations Consumers
Compliance Marketing
Improve Customer Service
Decrease C
hurn
Pro
duct
P
rom
otio
n
Product Design
CFPB G
uidelines
Com
plai
nts
Man
agem
ent
Improve customer
outcomes (e.g. loyalty, spend)
Enhance investment to
improve satisfaction
Proactive monitoring of
customer reactions
Identify and mitigate high
risk complaints
Refine channel marketing strategy
Proactive approach to regulators
ComplaintAnalytics
A complaint analytics system includes multiple use cases for each function
Complaint Analytics Applications
1
2
3
Text analytics to understand the voice of customer
Voice analytics to address complaint escalation
External lifestyle data to support complaint handling
Consumer treatments post fraud, disputes, complaints4
Text analytics can be used to understand the voice of the customer
Social Media
News
Call Transcripts and Agent Notes
Focus Groups
Online Chat
Collect structured and unstructured data streams across products and channels
1
Classify Text to Quantify Known Issues
Cluster Documents to Identify Emerging Issues
Analyze Sentiment and Top Keywords to Improve Predictive
Models
Integrate with Dashboards• Hypothesis Testing
• Emerging Trends/Surges
• Product Monitoring
Text analytics reveals origination experience drivers for wealth management customers1
A difficult process with poor communication leads to dissatisfaction. The bank in blue should focus on process
The top factors driving satisfaction and likelihood to recommend depend on competent bank staff
Source: Deloitte survey and analysis
Near real time voice analytics enables integration with predictive modeling and reporting
Customer Call
CSR
Customer
QueueRecordCalls
Voice Analytics (phonemes, text)
Contact Center
Customer Warehouse Prediction
Score
Sequence Based Predictive Model
CRM
Demo-graphic, Lifestyle Data
Customer Operations
Accelerators
Outbound or Transfer Call Center
Sample metrics• Wait time• Transfer rate• First contact resolution• Abandon rate• Escalation rate
IVR and Switch Logs
2
Escalated Complaints
Post Complaint Churn Repeat Callers CFPB Escalation
Near real time propensity model gives agents feedback
Sequence analysis identifies churn and reduced spending factors
Root cause and metric driven analysis to reduce callbacks
Join internal data with CFPB database to preempt escalation
Approach
• Agent desktop integration
• Specialist call center agents
• Outbound call center
• Real time retention offer generation
• Decisions based on customer lifetime value forecasts
• Cross channel context for agents
• Volume forecast and IVR changes
• Test and learn
• Reperform tests in CFPB examination manual
• Integrate with complaint responses
Implement
Model
Voice analytics can provide data for predictive models that prevent complaint escalation2
External lifestyle data can enhance the power of predictive analytics3
AcxiomAM BestAMAAmerican Housing SurveyAmerican Tort Reform FoundationBurueau of Labor StatisticsCap IndexCarfaxCDS Hail DatabaseCensus PointChoicepointCorporate Research BoardDataListerDirectory of US HospitalsDun & BradstreetEASI AnalyticsEEOCEquifaxESRIExperianFastcase Legal Research SystemFlorida Tax Assessment RecordsFulbright Lititgation Trends SurveyInsurance Information InstituteInsurance Institue for Highway SafetyInternal Renvue ServiceKnowlege Based Marketing (KBM)Lawyer Data – Florida & CaliforniaLexisNexisMartindale/Hubble Attorney ListingMRI Purchasing PropensitiesNFIRS – National Fire Reporting NHTSAOSHAUS Census
Wage DataWealth IndicatorsUnemployment StatsEEOC Complaints DBEc. Freedom Index Aggregated IRS DataOccupational Codes
Real Property DataAffluent HomeownersHome Equity BorrowerGovt. Housing SurveyHome Value ScoringForeclosure Data
Purchase BehaviorsPurchase PropensitiesSpend by Category DTC Spend by RetailerBrand Usage StatisticsRetailer Trans DataPurchase Triggers
Crime StatisticsHail Vector DataStorm Events DBClimate Data Geographic MappingCollege RankingsFirehouse Data Fire Incident Data
Judicial HellholesFed. Case Law DBFlorida Tax RecordsLit. Trends SurveyLawsuit Climate DataDUI/DWI LawsCA/FL Lawyer DataTort Liability Index
Bus. Hazard GradeBus.Insurance ScoresBus.Financial StatisticsUCC FilingsSmall Bus. DataBus. Credit ScoreOSHA Bus. Data Tax Liens & Bankruptcy
Disability DataUS Hospital DirectoryNursing Home DataMedical Provider DataHosptial Visit StatisticsDoctor Practice DataHealth Interest Data
Auto DataCarfax Vehicle HistoryMotor Vehicle ReportsAuto Injury / Loss DataDriver Device UsageRoad Rage SurveyVIN Decoding Data
Lifestyle and Life TraitsWorking MothersActive SeniorsHigh-Tech SegmentsLife Stage ClusteringDemo. Census Data
Data Vendors Data Vendors Data CategoriesData Categories
Augmenting outcomes monitoring with external data can improve complaint handling policy3
Source: Building Consumer Trust in Retail Payments, available at http://www.deloitte.com/assets/Dcom-UnitedStates/Local%20Assets/Documents/us_fsi_Bank_ConsumerTrustPayments_July08.pdf
Integrating analytics with operations• Proactive pre-complaint issue handling.
Some segments tend to have certain complaint issues.
• Perform segment based complaint resolution subject to fair treatment
• Develop a communication approach to reduce complaints and disputes
Example: customer A expressed interest in a simple resolution process without a notarized form. Subject to fraud loss risk guidelines, if Customer B is has similar lifestyle attributes to Customer A, omitting the notarized form can help build trust
Predicting how customers react after fraud handling is key to reducing complaints and churn4
Collect Data Build Analytics
Integrate with
Operations• Convert model results to reason
codes that correspond to process changes
• Provide service reps and complaint investigators with relevant context from previous interactions• Expedite a fraud investigation• Issue a credit for the
transaction(s) under investigation
• Retention offer as appropriate• Make recommendations about
the fraud handling policy based on predicted customer profitability and behaviors
• Customer data • Demographics• Product relationships• Profitability
• Contact event data • Potentially fraudulent
transactions• Consumer trust indices and
survey data derived from an existing Deloitte study
• Deloitte administered focus group (for updated data)
• Identify indicators that explain consumer behavior following a fraud incident
• Develop a propensity model that predicts customer churn, reduced spending, or inactive accounts• Who is likely to churn?• What are the risk factors?
• Analyze free form survey and focus group data to identify trends in unmet customer needs. For example• Ensured I didn’t lose money• Stopped transactions quickly• Limited paper forms• Went beyond minimum legal
requirements
Summary• The CFPB Complaint Database provides a starting point consumer complaint
analysis, but it can provide greater insights when properly integrated with internal process and complaint data
• Big data technologies are a key component of an enterprise-wide complaint analytics and response capability
• Text analytics can be applied to identify emerging issues and focus areas for improving customer satisfaction
• Using voice analytics to support predictive modeling represents an emerging area that can provide substantial incremental benefits to complaint analytics
• External data sources can augment a predictive model or provide context for customer interactions following complaints
Copyright © 2013 Deloitte Development LLC. All rights reserved.
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