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AI Agent Profile · LendingIQ · Bengaluru

Propensity Scoring Agent AI

Function: Collections AnalyticsInvocation: Daily before work-list generationRuntime: AWS Bedrock · ap-south-1Model: Claude Sonnet 4Context window: 200K tokens

DivisionCollections

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What this agent does

The Propensity Scoring Agent AI reads the daily delinquent account population — their CBS payment history, CRM contact response patterns, current bank balance signals from Account Aggregator, and bureau refresh data — and produces a ranked triage list telling the collections team which accounts to contact first, through which channel, and why. It is the analytical layer that converts a large delinquent book into a prioritised, actionable work-list for the Collections Head AI and the collections callers. Scores are triage ranks — not default probabilities, not credit grades, not provisioning inputs.

Primary functions

Pay-Propensity Scoring

Daily — all active delinquent accounts

Invoked when: daily data pipeline completes before Collections Head AI generates the day's work-list

  • Reads the available signals for each delinquent account and synthesises them into a propensity score: CBS payment patterns (recent partial payments are a positive signal; a clean miss with no prior delinquency is different from a chronic pattern); CRM contact response history (accounts that have responded to WhatsApp before are more likely to respond again; accounts with repeated non-contact are harder to reach); bank balance signals via AA where consent is active (a positive balance on the day of scoring is a direct capacity signal); and bureau refresh data (new tradelines at other lenders may signal financial stress or competing obligations).
  • Weights signals differently by DPD band: for DPD 0–30, bank balance and recent payment behaviour are the dominant signals. For DPD 31–90, contact response history and PTP compliance rate become more predictive. For DPD 90+, the propensity to settle rather than to pay in full requires a different signal set — the agent applies the appropriate weight configuration per bucket.
  • Produces a plain-language propensity narrative for each scored account — 2–3 sentences explaining why the account scored where it did. The narrative is the signal behind the rank, and it is the narrative the collections caller needs to have a productive conversation. A rank without a narrative is a number without context.
Output: Scored account register — propensity score (1–100 triage scale), score band (High / Medium / Low), 2–3 sentence propensity narrative per account, and the primary signals that drove the score for each account.

Account Prioritisation

Daily — ranked work-list for Collections Head AI

Invoked when: propensity scores are complete and the Collections Head AI requires a ranked list for work-list generation

  • Ranks accounts within each DPD bucket by propensity score — ensuring the highest-propensity accounts are contacted first within each bucket. Also applies a secondary ranking criterion: exposure size. Two accounts with similar propensity scores are ranked by outstanding exposure — the higher-exposure account is prioritised because the recovery value of success is greater.
  • Identifies the accounts that are approaching bucket boundary dates — DPD 27–29 accounts that will move to the mid-bucket, DPD 87–89 accounts that will classify as NPA in the next 1–3 days. These near-boundary accounts receive a priority flag regardless of their propensity score, because the cost of missing a recovery opportunity in the final days before bucket transition is higher than for accounts with more time remaining.
  • Flags accounts where the propensity score has changed materially since yesterday's run — a large upward movement (bank balance improved, payment received at another lender) is an opportunity signal worth acting on immediately; a large downward movement (bank account drained, new delinquency on bureau) warrants a same-day review for escalation.
Output: Ranked prioritisation list — accounts sorted by propensity within DPD bucket, exposure-adjusted secondary ranking, bucket-boundary flags, material score change alerts (up and down), and a daily collections capacity estimate showing how many accounts the team can realistically work given the day's prioritised list.

Channel Selection

Per account — based on response history and profile

Invoked as part of the prioritisation run — channel recommendation generated alongside each account's rank

  • Recommends the optimal first-contact channel for each account based on its prior response history: an account that has responded to WhatsApp in the past 30 days receives a WhatsApp recommendation; an account that has never responded to WhatsApp but has responded to IVR calls is recommended for IVR first. Where no prior response history exists, defaults to WhatsApp as the lowest-friction first contact.
  • Flags channel constraints: accounts where the borrower has unsubscribed from WhatsApp (do not contact via WhatsApp), accounts where the phone number is inactive or disconnected (flag for address update before contact), and accounts where TRAI DND status prevents automated messaging (route to human caller only).
  • Recommends human caller contact for accounts flagged as requiring empathy or judgment — prior hardship indicators, accounts the Early Warning Agent AI has flagged as stress-signalled, or accounts where the propensity score narrative indicates financial complexity that an automated channel cannot handle.
Output: Per-account channel recommendation — first contact channel, rationale based on response history, channel constraints noted, and human-caller flag for accounts where automation is not appropriate.

Hard guardrails

Will notProduce calibrated default probabilities for provisioning or capital calculations. Propensity scores are triage ranks for operational prioritisation — they are not actuarially validated PD estimates.
Will notContact borrowers — this is a purely internal analytics agent. All contact is executed by the Early Bucket, Mid Bucket, or Hard Bucket agents based on this agent's prioritised list.
Will notOverride the Collection Head AI's work-list prioritisation with its own ranking. The propensity score is an input to the Collections Head AI — the Collections Head AI applies strategy considerations (capacity, geographic clustering, agent skill matching) on top of the propensity rank.

Known limitations

AA data coverage gaps create score quality differences. Accounts without active AA consent fall back to CBS payment data and bureau signals only — their scores are less granular and potentially less accurate than AA-enabled accounts. The score output labels the data completeness level per account so the collections team knows which scores are bank-balance-informed and which are not.Track the AA consent rate in the active delinquent book as a collections analytics KPI. Each additional consented account improves propensity score quality across the entire DPD portfolio.
Signal weights in the scoring calibration corpus require quarterly review against actual outcomes. If the collected portfolio shifts in mix — new segments, new product types, changing macro environment — weights calibrated on prior cohorts will produce systematically mis-ranked accounts. The scores will still rank accounts, but the ranking will be less accurate.Run a quarterly back-test: compare the prior quarter's high-propensity accounts against their actual payment outcomes. Where high-propensity accounts underperformed and low-propensity accounts outperformed, the signal weights need recalibration. This is the primary quality control mechanism for the scoring function.
Agent Profile · Propensity Scoring Agent AI · LendingIQ · BengaluruLast updated April 2026 · For internal use

Important Reads

Learn more about how to deploy Propensity Scoring Agent AI to your lending workflow.