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PricingOptimizer™ - Dynamic Rate Intelligence

Real-time rate optimization balancing credit risk, market competition, and profitability targets.

The Challenge

Set rates too high and lose volume to competitors. Set them too low and leave profit on the table or attract adverse selection. Static rate sheets can't optimize this tradeoff.

What It Does

Real-time rate optimization balancing credit risk, market competition, customer lifetime value, and profitability targets to maximize portfolio returns.

Model Metadata

  • Model Architecture: Multi-armed bandit reinforcement learning with contextual features
  • Training Data Size: 10M+ rate quotes and outcomes (accept/decline decisions)
  • Features: 180+ borrower, market, and competitive features
  • Update Frequency: Real-time learning from every quote
  • Inference Speed: <100ms for optimal rate calculation
  • A/B Testing: Continuous experimentation (5-10% exploration rate)

Business Outcomes

  • 15% improvement in risk-adjusted portfolio yield
  • 22% increase in approval-to-funding conversion
  • Better volume/margin tradeoff optimization
  • $12M+ additional annual revenue (typical $500M originations)
  • Reduced adverse selection (3% lower early default rates)
  • Competitive win rate increased 18%

Training Approach

Contextual BanditsReinforcement learning approach that learns which rates to offer based on borrower context (risk profile, loan characteristics, channel). Balances exploration (testing new prices) vs. exploitation (using known good prices).
Causal InferenceEstimates true price elasticity by analyzing past rate experiments and natural experiments (rate changes over time).
Counterfactual ModelingFor every loan originated, models what would have happened at different rate levels to improve price-response curves.
Multi-Objective OptimizationBalances competing objectives: acceptance rate (volume), profit margin, portfolio quality (avoiding adverse selection), customer lifetime value.
Competitive PositioningIncorporates competitor rate intelligence to optimize win rates vs. profitability tradeoff.

Data Sources

Borrower Risk Data
  • Credit scores and risk tiers (from CreditSight™)
  • Default probabilities and expected loss
  • Income stability and debt ratios
  • Collateral and loan-to-value
Customer Value Data
  • Lifetime value predictions (cross-sell potential)
  • Existing relationship depth
  • Acquisition cost by channel
  • Retention and refinance probabilities
Market & Competitive Data
  • LendingTree, Bankrate (published rates)
  • Optimal Blue (rate lock data for mortgages)
  • Peer rate intelligence from loan applications
  • Won/lost deal analysis with competitive rates
Economic Factors
  • Treasury yield curves
  • Mortgage-backed security prices
  • Fed funds rate and policy expectations
  • Secondary market pricing
Channel Data
  • Acquisition channel (direct, broker, affiliate)
  • Marketing campaign and creative
  • Traffic source and attribution
  • Time of day, day of week seasonality
Operational Data
  • Funding costs and spreads
  • Servicing costs by loan type
  • Target profit margins by product
  • Pipeline volume and capacity
Third-Party Intelligence
  • Black Knight Optimal Blue (mortgage rate data)
  • Curinos (deposit and loan pricing intelligence)
  • J.D. Power (consumer shopping behavior)

Advanced Capabilities

  • Real-time rate adjustments based on pipeline volume
  • Personalized pricing reflecting true risk + customer value
  • Dynamic rate locks (shorter locks for lower-risk borrowers)
  • Channel-specific optimization (direct vs. broker)
  • Seasonal and day-of-week pricing variations