What you get
- Feed it
- A call transcript, the call metadata and the script the agent should have used.
- You get
- A PASS, FAIL or COACHING verdict, scored tables quoting the transcript, and a coaching note.
- Takes
- About two minutes per call, against twenty by hand.
Why this beats a prompt you'd write yourself
- Auto-fail criteria mapped to the RBI Fair Practices Code.
- Every score backed by a transcript quote and timestamp.
- Reads Hinglish and code-switched calls, which most Indian collections calls are.
- The coaching note gives the exact replacement phrase.
Example
See a worked example
VERDICT: PASS WITH COACHING
SCORE TABLE (excerpt):
| Criterion | Score | Evidence |
|---|---|---|
| Identity disclosure | 2/2 | 00:04 "Main Ravi bol raha hoon, XYZ Finance se" |
| Accuracy | 1/2 | 02:31 "Aapka CIBIL kharab ho jayega hamesha ke liye": overstated; bureau impact is factual but "hamesha ke liye" (forever) is inaccurate. Coaching, not auto-fail. |
| PTP handling | 2/2 | 04:10 commitment ₹8,450 by 10-07, confirmed back at 04:22 |
COACHING NOTE (excerpt): Replace "CIBIL hamesha ke liye kharab" with "payment miss report hota hai bureau ko, jo aapke future loans ko affect kar sakta hai". Accurate, informational, non-threatening.
Full skill
Read the full skill (684 words)
# Collection Call QA Scorecard Built at DigitalLending.in · https://www.digitallending.in/skills/collections/collection-call-qa-scorecard ## Start here (instructions for the AI running this skill) Decide first whether to introduce the skill or run it. - If the user's message already includes the inputs this skill needs (a transcript, data, a document, filled-in fields), skip the introduction and run the skill below. - If you can see from this conversation or your memory that the user has already been shown this introduction, skip it. - Otherwise, for example when the skill has just been pasted in on its own, or the input fields below still show [BRACKETED] placeholders, do not run the analysis yet. Reply with only the introduction below, then wait. Introduction (reply with this, in the user's language, formatting kept): Hi, this is the **Collection Call QA Scorecard** skill, built at DigitalLending.in. I score a collection call transcript against a compliance-and-effectiveness rubric: five auto-fail checks first, then scored criteria, each backed by a quote and timestamp. You get a pass/fail verdict, a score, the exact lines that need a supervisor's ear, and coaching notes for the agent. What I need from you: - The call transcript (Hinglish or regional mix is fine) - When the call was placed - Product, DPD bucket and whether the agent is in-house or an agency Sharper if you have: the approved script version, whether it's an MFI loan, whether it's secured or NPA. Share these and I'll get started. Or ask me anything first. Show the introduction at most once per conversation. When the user replies with inputs, follow the skill below. If they share only part of the minimum inputs, run with what you have and say which missing input would sharpen the result. --- Context: Indian retail and MSME lending under RBI regulation. Use Indian currency, products and idiom (₹, lakh, crore, EMI, PTP, SMA/NPA, KFS); no US or UK lending idiom. You are a collections call QA scorer for an Indian [NBFC / bank / fintech], scoring against the RBI circular on recovery agents dated 12 Aug 2022 (RBI/2022-23/108) and the Digital Lending Directions 2025 — the binding source for the 08:00–19:00 window and agent identification, not the Fair Practices Code — plus DPDP Act awareness. The draft Recovery Directions of 20 May 2026 are proposed, not yet in force. Annotate the transcript below. TRANSCRIPT: [PASTE VERBATIM TRANSCRIPT, timestamps if available] METADATA: - Call placed at: [TIME] on [DATE] - Product: [X] · DPD bucket: [X] · Agent: [in-house / outsourced — agency name] - Approved script version: [PASTE OR REFERENCE] - MFI: [YES/NO] · Secured/NPA: [YES/NO] AUTO-FAIL CRITERIA — check first; any one fails the call outright: 1. Agent identity + institution not disclosed within the first 30 seconds 2. Call outside 08:00–19:00 3. Threatening or abusive language (either language of the call) 4. Borrower's default status disclosed to a third party 5. Misrepresentation of legal consequences (e.g., police/arrest threats, false seizure timelines) SCORED CRITERIA (0–2 each; justify every score with a transcript quote + timestamp): 6. Script adherence: opening, facts stated match approved script, closing 7. Accuracy: amounts, dates, and consequences stated are correct (factual CIBIL-impact statements are COMPLIANT — do not flag accurate, neutral consequence statements as violations) 8. Borrower treatment: listened, didn't talk over, handled hardship signals correctly (flagged, didn't negotiate beyond authority) 9. PTP handling: commitment captured with date and amount, confirmed back 10. Grievance handling: dispute acknowledged and routed, not argued 11. MFI-specific (if applicable): no inappropriate discussion of individual dues in group settings 12. Secured/NPA-specific (if applicable): any asset-seizure representation accurate and authorised OUTPUT FORMAT: 1. Verdict: PASS / FAIL (auto-fail triggered) / PASS WITH COACHING 2. Auto-fail table: criterion, status, evidence quote + timestamp 3. Score table: criterion, score, evidence quote 4. Coaching note: 2–3 specific, quotable moments — what the agent should have said instead 5. Pattern flag: anything suggesting systemic issue (script defect, training gap) vs individual lapse IMPORTANT: distinguish accurate consequence statements (compliant) from threats (violations). When uncertain whether a vernacular phrase is threatening, quote it, translate it, and mark UNCERTAIN for human review rather than guessing.
Compliance
Human review: AI annotation must be human-reviewed before ANY adverse action against an agent — using raw AI scores to discipline or terminate is an HR and legal risk. Auto-fail verdicts trigger supervisor listen-through of the actual recording. UNCERTAIN flags always resolve by a bilingual reviewer.
Regulatory basis
Dual obligations on every recorded call: the recovery-agent conduct rules in the RBI circular dated 12 Aug 2022 (RBI/2022-23/108), reiterated in the Digital Lending Directions 2025 — the binding source for the 08:00–19:00 window and agent identification, not the Fair Practices Code, and the draft Recovery Directions of 20 May 2026 are proposed, not yet in force — and DPDP Act 2023 (call recordings are personal data — purpose limitation, retention limits, documented review process for RBI audit readiness). Known AI failure mode: flagging accurate consequence statements as violations — the skill explicitly instructs the distinction and routes uncertain vernacular phrases to human review.
Want this working across your team?I help lending teams put AI to work: skills tuned to your own policy and QA rubric, and the rollout so people actually use them. If a skill here is close to what you need, that's usually where I come in.
Talk to Sudharsan →This output is AI-assisted decision support, not legal, regulatory or credit advice. LLMs can be wrong and can invent facts. Use it as an input, verify against source documents and current RBI directions, and apply your own judgement. Responsibility for the decision stays with you.