Mis-Selling and Disclosure Testing for Insurance Bots

• Chief Compliance Officer
• Head of Digital Sales
• Chief Information Security Officer
• Product Owner, Conversational AI

Insurers, brokers and aggregators now sell and service policies through chatbots and voice bots built on large language models. A bot that calls a ULIP risk-free, skips a waiting period, suggests leaving out a pre-existing condition or offers a discount it cannot give creates the same mis-selling exposure as a human seller, at far greater scale.

We run a structured red-team engagement against your sales or service bot and leave behind a regression suite. We start from your product documents, such as the customer information sheet, brochure, policy wording and benefit illustration, and turn them into expected disclosures and prohibited claims. We then run multi-turn adversarial conversations in English, Hindi and Hinglish, by text and by synthetic voice calls, using personas such as a senior citizen, a first-time buyer and an aggressive negotiator. Each conversation is scored PASS, RISK or FAIL against a rubric your compliance team signs off, and every finding comes with the transcript or audio and a suggested fix.

• Builds expected-disclosure and prohibited-claim lists from your product documents and scripts
• Runs multi-turn adversarial playbooks covering mis-selling, disclosure, prompt injection and data leakage
• Tests text bots through their API or web widget and voice bots through synthetic calls in Hindi and Hinglish
• Scores each conversation PASS, RISK or FAIL with an evaluation model, with people adjudicating borderline cases
• Maps security findings to the OWASP Top 10 for LLM Applications
• Packages the scenarios as a regression suite for every future bot release

1. 01Agree scope and rubricBots, products, channels and the PASS, RISK and FAIL rubric are agreed with compliance and product owners.
2. 02Generate scenariosProduct documents and complaint themes are turned into scenarios, personas and attack variations.
3. 03Run conversationsMulti-turn conversations are run against the staging bot by text and synthetic voice.
4. 04Score and adjudicateAn evaluation model scores each transcript; uncertain and RISK results go to a compliance reviewer.
5. 05Report with evidenceFindings are grouped by severity and category, with transcripts, audio and recommended fixes.
6. 06Re-test and automateFixes are re-tested, and the scenario set joins your release pipeline as a regression suite.

1. Scenario design
   • Product fact extractor for CIS, brochure and wording
   • Rubric builder
   • Persona and attack library in English, Hindi and Hinglish
2. Execution
   • Bot API and web widget connector
   • Synthetic voice caller over telephony
   • Multi-turn conversation orchestrator
3. Evaluation
   • Rubric-based evaluation model
   • Rule checks for prohibited phrases and missing disclosures
   • Compliance adjudication queue
4. Reporting and regression
   • Evidence store for transcripts and audio
   • Findings report mapped to OWASP LLM Top 10
   • CI regression suite

Compliance officers sign off the rubric and adjudicate every RISK result and any score the evaluation model is unsure about. Product and engineering owners decide which fixes to make and when to re-test.

• Staging chatbot API or web widget
• Telephony test numbers or SIP trunk for voice bots
• CI/CD pipeline such as GitHub Actions or GitLab CI
• Issue tracker such as Jira
• GRC or compliance evidence repository

• Testing only on staging or agreed test windows, under a written scope
• Synthetic personas and data only; no real policyholder information
• Rate limits so testing does not affect shared infrastructure
• Evidence stored with access control and agreed retention
• Evaluation model results sampled and checked by people

• The rubric draws on IRDAI expectations on fair disclosure, advertisements and policyholder protection; findings are evidence for your compliance team, not a regulatory certification.
• Security findings map to the OWASP Top 10 for LLM Applications and can feed a NIST AI RMF-style risk register.

• Insurance

  Policy Renewal Voice Agent

  A multilingual voice agent that calls policyholders before renewal, explains premium and cover from approved content, sends a UPI payment link and hands complex requests to licensed staff.

• Insurance

  Policy Wording Comparison Assistant

  Align two or more policy wordings clause by clause, highlight what was added, removed, narrowed or broadened, and answer questions with citations to the exact clause.

• Banking

  Red-Teaming for Bank Chatbots and Voice Agents

  Adversarial testing of bank chatbots and voice agents for prompt injection, data leakage, account-takeover social engineering and unsafe advice, in English, Hindi and Hinglish.

• Telecom & Contact Centres

  Voice Agent Red-Teaming and Jailbreak Testing

  Adversarial phone calls against your voice agent in Hindi, Hinglish and English, testing verification bypass, data leakage, prompt injection and off-script promises, with recorded evidence.