5 BRE use cases you might have missed – Webinar recap & insights

In today’s ultra-competitive lending environment, speed, consistency, and agility in decision-making are no longer optional—they’re survival requirements. A recent webinar hosted by Celusion Technologies’ CEO, Pravin Paulose, explored how a Business Rule Engine (BRE) can automate and improve decision points across the lending lifecycle—not just in underwriting, but in collections, payouts, and cross-selling.

Joining him was Rakesh Das, Chief Credit Officer at Tiger Capital, who shared first-hand insights from implementing a BRE at scale.

While BRE adoption in credit and lending is well established, here are five powerful use cases we explored in the session.

Use Case 1 – Collections

Many lenders still rely on a “one-size-fits-all” collections approach. With rising regulatory scrutiny and evolving customer expectations, that’s no longer effective.

Challenges:

     • Manual risk bucketing (low/medium/high risk)

     • Static, unchanging workflows

     • Delayed follow-ups leading to higher delinquency rates

How a BRE Helps:

     • Dynamically segments customers by risk profile

     • Tailors strategies for each bucket

     • Reassesses cases daily or on key events

     • Creates feedback loops to track success

Example: Using a decision tree, customers are bucketed by balance category, repayment history, and contactability. Risk levels are calculated instantly, and strategies update automatically.

Benefits: Faster collections, better recovery rates, smarter resource allocation, and rapid strategy adjustments.

Use Case 2 – Payouts & Incentives

Partner payouts and employee incentives can get complicated—especially when managed manually in spreadsheets.

Challenges:

     • Errors from manual calculations

     • Lack of payout transparency

     • Delayed disbursements lowering motivation

How a BRE Helps:

     • Real-time eligibility checks for payouts

     • Complex slabs, caps, and seasonal bonuses handled automatically

     • Multi-layer decision tables to calculate targets and adjustments for new joiners

Example: A BRE-driven incentive policy calculates targets by role and location, adjusts for tenure, and determines the final payout percentage—all in real time.

Benefits: Faster, error-free payouts, motivated teams, and effortless policy changes.

Use Case 3 – Cross-Selling & Smart Offers

Mass, untargeted offers waste effort and yield poor conversions.

Challenges:

     • Static segmentation

     • Poor timing of offers

     • Lack of personalisation

How a BRE Helps:

     • Rule-based eligibility checks for each customer

     • Contextual timing (e.g., insurance renewal offers based on loan start dates)

     • Channel personalisation using past response patterns

Benefits: Higher conversions, lower marketing costs, and optimised resource use.

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‍Use Case 4 – Underwriting

Underwriting is the most common starting point for BRE adoption, but its full potential is often underused.

Challenges:

     • Slow manual review of applications

     • Inconsistent policy application across teams

     • Difficulty updating criteria quickly

How a BRE Helps:

     • Automates go/no-go decisions using eligibility and deviation rules

     • Integrates bureau data, asset valuation, and product matching in a single workflow

   • Enables Straight-Through Processing (STP) for low-risk applications while routing exceptions for manual review

Example: A BRE can instantly evaluate bureau reports, validate documents, assess assets, and apply risk-based pricing—all without developer intervention.

Benefits: Faster TAT, reduced human error, improved compliance, and consistent decision-making.

Use Case 5 – Pricing & Risk-Based Offers

Pricing decisions in lending require balancing profitability with regulatory compliance.

Challenges:

     • Static rate cards that don’t reflect risk profiles

     • Manual calculations leading to delays

     • Limited ability to run “what-if” pricing scenarios

How a BRE Helps:

     • Uses decision tables to map credit score, income, and debt-to-income ratio to interest rate and fee bands

     • Adjusts pricing dynamically for campaigns or regulatory changes

     • Supports transparent audit trails for why a customer received a particular rate

Example: If a customer’s debt-to-income ratio is below 40% and score is above 750, the BRE can instantly assign the appropriate interest rate and processing fee—fully documented for audit purposes.

Benefits: Competitive, fair, and profitable pricing delivered instantly at the point of decision.

Guest Insights – Tiger Capital’s Experience

Business Impact:

     • 83–88% of cases decisioned within Day 0 or Day 1

     • Reduced audit errors (especially missed deviation capture)

     • Risk-based pricing aligned with regulations

     • Automated collections communications and bucket allocations

Future Focus: Expanding cross-sell strategies to boost bottom-line growth without extra customer acquisition spend.

Rakesh’s Advice for Selecting a BRE:

     1. Usable by business/product teams without coding

     2. Quick deployment of rule changes

     3. Flexible for new products and use cases

     4. Scalable for large volumes

     5. Simulation/testing before go-live

BREs are no longer just underwriting tools—they’re strategic engines powering faster decisions, better compliance, and new revenue streams. As Rakesh Das put it: “A BRE’s potential is only limited by your imagination. Once you see success in one area, you’ll find more places to apply it.”