DECISIONING REINVENTED
Real-World AI Use Cases Across the Customer Lifecycle
Introduction
This playbook helps any business offering a financial services product unlock smarter decisions with AI, one use case at a time.
HOW TO USE THIS PLAYBOOK
1. Start with your priority area: Onboarding, Fraud, Customer Management, or Collections
2. Choose a use case aligned to a business goal: Faster approvals, lower fraud, better margins
3. Review the data, execution tips, and outcomes
4. Pick one to pilot, prove, and scale
1. INSTANT CREDIT DECISIONING
ONBOARDING
USE AI TO ACCELERATE TIME TO “YES,” REDUCE DROP-OFF, AND REACH NEW MARKETS.
WHY IT MATTERS: Customers expect real-time decisions. AI combines data sources to make smarter, faster approvals.
DATA REQUIRED:
• Credit bureau data
• Income/affordability metrics
• Open banking feeds
• ID and fraud signals
BEST FOR:
Lenders, BNPL, cards, microfinance, embedded finance
EXPECTED IMPACT:
• Reduction in decisioning time
• Increased approval rates
• Lower abandonment
2. ALTERNATIVE DATA UNDERWRITING
WHY IT MATTERS: Many creditworthy applicants are thin- or no-file. AI helps underwrite them responsibly.
DATA REQUIRED:
• Telco, utility, rental data
• Gig economy and income flows
• Mobile usage, psychometric scores
BEST FOR:
Emerging markets, fintechs, inclusive finance
EXPECTED IMPACT:
• Increase in addressable market
• Reduced manual review
• More responsible inclusion
3. IDENTITY & ELIGIBILITY VERIFICATION
WHY IT MATTERS: Verifying the right customers fast, while preventing fraud and staying compliant, remains an onboarding hurdle.
DATA REQUIRED:
• Core Personal Identifiable Information (PII)
• Document-Based Verification (ID Doc)
• Biometric Data (Selfie/face matching)
• Digital and Device Data (Device ID)
• Bureau Data (ID Footprint)
• Enrichment Data (Email, Mobile & Social Network)
BEST FOR: Any business with digital onboarding
EXPECTED IMPACT:
• Enhanced Customer Experience
• Stronger Regulatory Compliance
APPLICATION FRAUD
4. FIRST PARTY APPLICATION FRAUD DETECTION
WHY IT MATTERS: Fraud tactics evolve rapidly, use AI to learn and prevent fraud in real-time.
DATA REQUIRED:
• Customer PII
• Bureau Data (Income Validation & Activity)
• Digital and Device Data (Device ID)
STOP FRAUD EARLIER, WITH LESS FRICTION AND GREATER ACCURACY.
• Digital and Device Data (Device ID)
• Fraud Data (Consortium/Historic Frauds)
• Behavioral Biometrics
• Enrichment Data (Email, Mobile & Social Network)
• Historic Application Data (Profiling)
BEST FOR:
Any business with digital onboarding
EXPECTED IMPACT:
• Better fraud detection (Reduced First Party Losses)
• Reduce friction for better customer experience
• Improve operational efficiency
• Enhanced Business Intelligence
5. SYNTHETIC IDENTITY DETECTION & IMPERSONATION
WHY IT MATTERS: Synthetic identities are hard to spot, uncover hidden threats by detecting subtle, suspicious signals.
DATA REQUIRED:
• Customer PII
• ID&V
• Fraud Data (Consortium/Historic Frauds)
• Behavioral Biometrics
• Facial Recognition
• Enrichment Data (Email, Mobile & Social Network)
• Historic Application Data (Profiling)
BEST FOR:
Any business with digital onboarding
EXPECTED IMPACT:
• Better fraud detection (Reduced First Party Losses)
• Reduce friction for better customer experience
• Improve operational efficiency
• Enhanced Business Intelligence
6. DYNAMIC RISK-BASED PRICING
CUSTOMER MANAGEMENT
IMPROVE ENGAGEMENT, PROFITABILITY, AND PERSONALIZATION WITH ADAPTIVE, AI-POWERED STRATEGIES.
WHY IT MATTERS: AI lets you offer the right product, to the right customer, at the right price, profitably.
DATA REQUIRED:
• Credit and income
• Risk model outputs
• Usage patterns
• Segmentation and offer history
BEST FOR:
Loans, credit lines, subscription products
EXPECTED IMPACT:
• Increased margin per customer
• Higher acceptance of offers
• Smarter tiering
7. CREDIT LINE MANAGEMENT
WHY IT MATTERS: AI lets you offer the right product, to the right customer, at the right price, profitably.
DATA REQUIRED:
• Utilization trends
• Repayment consistency
• Macroeconomic indicators
BEST FOR:
Cards, BNPL, overdrafts
EXPECTED IMPACT:
• Lower default risk
• Increased wallet share
• Better customer experience
Customer Management
8. PERSONALIZED RETENTION OFFERS
WHY IT MATTERS: Use AI to keep high-value customers with relevant, timely incentives.
DATA REQUIRED:
• Tenure, spend behavior
• Retention history
• NPS or churn signals
BEST FOR:
Retail banking, fintech apps, wealth platforms
EXPECTED IMPACT:
• Reduced churn
• Increased customer lifetime value
• Improved campaign ROI
RECOVER SMARTER. PRIORITIZE THE RIGHT CUSTOMERS, STRATEGIES, AND TIMING TO REDUCE NPLS.
9. PRE-DELINQUENCY DETECTION
WHY IT MATTERS: Predict payment issues early to intervene before default.
DATA REQUIRED:
• Payment behavior
• Spending changes
• External triggers (e.g., industry/job risk)
BEST FOR:
Personal lending, auto, home, credit card
EXPECTED IMPACT:
• Fewer delinquencies
• Better customer retention
• Lower NPL ratio
10. INTELLIGENT COLLECTIONS SEGMENTATION
WHY IT MATTERS: Not all delinquent accounts are the same. AI personalizes recovery paths by intent and risk.
DATA REQUIRED:
• Historical repayments
• Communication responsiveness
• Risk scoring
BEST FOR:
Any product with a repayment cycle
EXPECTED IMPACT:
• Higher recovery rates
• Lower ops costs
• Less reputational risk
Collections
11. CHANNEL OPTIMIZATION FOR RECOVERY
WHY IT MATTERS: AI determines which communication methods work best for each borrower: SMS, call, email, app, or agent.
DATA REQUIRED:
• Past channel success
• User preferences
• Engagement trends
BEST FOR:
Scale lenders, digital banks, call centers
EXPECTED IMPACT:
• Improved contact rates
• Faster repayment resolution
• Enhanced collections experience
12. ROLL RATE RISK FORECASTING
WHY IT MATTERS: AI can model who is likely to “roll” from early delinquency into charge-off.
DATA REQUIRED:
• Time series payment data
• Economic exposure
• Customer-level risk shifts
BEST FOR:
Mature portfolios with repayment friction
EXPECTED IMPACT:
• Better loss forecasting
• Prioritized collections effort
• Improved provisioning accuracy
MAKING THE PLAYBOOK WORK FOR YOU
3 STEPS TO ACTION:
Choose 1–2 use cases aligned with business pain points
Check your data readiness
Pilot fast, measure early wins, then scale
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