VIPRATECH LABS

Conduct Testing for Collections Voice Agents

Test collections voice agents, in-house or vendor-built, for harassment, third-party disclosure, misrepresentation and vulnerable-borrower handling in Hindi and Hinglish, with audio evidence.

Who it is for

  • Chief Compliance Officer
  • Head of Collections
  • Chief Risk Officer
  • Head of Vendor and Outsourcing Risk

The problem

Lenders are buying or building AI voice agents for reminders and collections, and RBI holds the lender responsible for the conduct of anyone collecting on its behalf. A voice agent can be pushed into saying things no trained collector would, by an angry borrower, a confused relative or a deliberate attempt at manipulation, and vendor demos rarely show those cases.

Conduct failures under pressure

Borrowers who argue, plead or provoke can draw a model into threats, sarcasm, false urgency or claims about legal action.

Third parties on the line

Spouses, parents, employers or neighbours who answer may hear details of the debt if the agent handles identity poorly.

Vulnerable borrowers

Mentions of illness, bereavement, job loss or distress need recognition and a humane response, which scripted tests seldom cover.

No independent evidence

Compliance teams need repeatable evidence of how the agent behaves, not just the vendor's assurance and a handful of sample calls.

What we build

We run a conduct test of your collections voice agent in a test environment, using synthetic borrowers and accounts. Persona agents place realistic calls in Hindi, Hinglish, English and selected regional languages, playing angry, evasive, confused, vulnerable and manipulative borrowers, and third parties such as a parent or an employer. Multi-turn playbooks cover threat and harassment triggers, disclosure to third parties, misrepresentation as police or court, pressure to pay into other accounts, calls outside permitted hours, dispute and hardship handling, and prompt injection by speech. Each call is recorded, transcribed and judged against your conduct rules and RBI expectations, with a PASS, RISK or FAIL verdict and evidence.

  • Persona-driven test calls by voice in Hindi, Hinglish, English and selected regional languages
  • Playbooks for threats, shaming, false legal claims and misrepresentation
  • Third-party and right-party scenarios that test disclosure before identity confirmation
  • Vulnerable-borrower and hardship scenarios with expected escalation behaviour
  • Calling-window, frequency and opt-out checks against the agent's scheduler
  • PASS, RISK and FAIL verdicts with audio, transcripts and mapping to conduct rules

How it works

  1. 01Agree the conduct rulebookWe turn your collections policy, Fair Practices Code commitments and RBI expectations into testable rules.
  2. 02Set up the test environmentSynthetic borrowers, accounts and phone numbers are configured so no real customer is contacted.
  3. 03Run persona callsPersona agents place multi-turn voice calls across scenarios and languages, recording every exchange.
  4. 04Judge each callAutomated judges score transcripts and audio against the rulebook, and human reviewers validate every RISK and FAIL.
  5. 05Report with evidenceFindings include audio clips, transcripts, the rule breached and remediation guidance for your team or vendor.
  6. 06Retest and regressFixed scenarios are rerun, and the suite becomes a regression test for future releases.
Conduct Testing for Collections Voice Agents: workflow diagram
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Architecture

  1. Rulebook and setup
    • Conduct rulebook from policy and RBI expectations
    • Synthetic borrower and account data
    • Test phone numbers and environment
  2. Persona generation
    • Supervisor agent selecting scenarios
    • Persona agents for borrowers and third parties
    • Multilingual and code-mixed speech synthesis
  3. Call execution
    • Outbound or inbound test telephony
    • Call recording and transcription
  4. Evaluation
    • Automated conduct judges
    • Human validation of RISK and FAIL verdicts
    • Mapping to conduct rules and AI risk frameworks
  5. Reporting
    • Evidence store with audio and transcripts
    • Findings report and regression suite
Conduct Testing for Collections Voice Agents: architecture diagram
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Inputs and outputs

Inputs

  • Access to the voice agent in a test environment with test numbers
  • Collections policy, scripts and escalation rules
  • Fair Practices Code commitments and internal conduct standards
  • Synthetic borrower profiles and account data
  • Vendor documentation of guardrails, where available

Outputs

  • Conduct rulebook used for testing
  • PASS, RISK or FAIL verdicts per scenario
  • Audio clips and transcripts for every finding
  • Remediation guidance for the lender or vendor
  • Regression suite for future releases

Where people stay in control

Human reviewers validate every RISK and FAIL verdict before it is reported, and your compliance team approves the rulebook. Whether to launch, fix or withdraw the agent is the lender's decision.

Integrations

  • Voice agent test endpoint or test phone numbers
  • Vendor issue tracker
  • GRC or compliance testing records
  • CI pipeline for regression runs, if required
  • Call recording storage under the lender's policy

Guardrails

  • Synthetic borrowers and test numbers only; no real customer is ever called
  • Recordings and transcripts stored encrypted with restricted access
  • Every RISK and FAIL verdict validated by a person
  • Rulebook versioned and approved by compliance before testing
  • Verdicts describe observed behaviour and do not certify compliance

Compliance notes

  • Helps lenders evidence oversight of AI agents collecting on their behalf, in line with RBI recovery-agent and outsourcing expectations.
  • Findings can be mapped to the OWASP Top 10 for LLM Applications, NIST AI RMF and RBI FREE-AI themes for governance reporting.

A typical pilot

Duration: 3 weeks

One collections or reminder voice agent tested in Hindi and Hinglish across a focused set of scenarios: third-party disclosure, threats, vulnerable borrowers and disputes.

Deliverables

  1. 01Conduct rulebook approved by compliance
  2. 02Executed persona calls with recordings
  3. 03Findings report with verdicts and evidence
  4. 04Remediation session with your team or vendor
  5. 05Retest and regression suite

What we measure

Verdicts by scenario and language
PASS, RISK and FAIL counts per scenario and language, each backed by evidence.
Disclosure before identity confirmation
Calls where the agent revealed account details before confirming the right party.
Escalation behaviour
Vulnerable-borrower and dispute scenarios where the agent transferred or offered a human callback as the rulebook requires.
Fix verification
Failed scenarios rerun after fixes, split into resolved and still reproducible.

Built on related work

AutoSentinx: voice agent red-teaming

VipraTech product

Autonomous red-team and compliance testing for Hindi and Hinglish voice agents, with multi-turn adversarial playbooks and PASS/RISK/FAIL evidence mapped to OWASP LLM and agentic risks, MITRE ATLAS, NIST AI RMF, and RBI, DPDP and TRAI-style requirements.

FAQs

01Can you test a vendor's voice agent?

Yes, with access to a test number or endpoint and the vendor's cooperation on fixes. The lender receives independent evidence and the vendor receives reproducible cases.

02Are real borrowers ever called?

No. All calls go to and from test numbers with synthetic borrower data. If your agent can run only on production systems, we first agree an isolated configuration.

03Which languages do you test in?

Hindi, Hinglish and English as standard, with Marathi, Tamil, Telugu, Bengali and others added based on where your agent operates. Code-mixed speech is included because real calls are rarely in one language.

04Is this a one-off test?

It can be, but most value comes from rerunning the suite after every change to the model, prompts or scripts, so conduct does not regress unnoticed.

Related use cases

Akhilesh Mittal

Founded by Akhilesh Mittal. 8 years building a security platform for a New Zealand company. IIT ISM Dhanbad and IMT Ghaziabad. Builds production agentic AI.

Test one lending workflow on your own files

Book a discovery call to scope a short pilot, from statement analysis to a conduct-safe reminder agent, and judge it on measured results before scaling.