2026 Guide: How to Use Call Data to Improve Credit Decisions

TL;DR

Call data is not one thing. It spans three distinct layers: telecom metadata (call detail records), conversational content extracted from actual calls, and speech analytics signals like tone and sentiment. Each layer feeds different features into credit decision models. For the 500 million+ Indians without a credit score, call data fills gaps that bureau data simply cannot. This guide breaks down every key term, walks through the practical pipeline from raw calls to model inputs, and explains what Indian regulations actually allow.

Why Call Data Matters for Credit Decisions

Over 50 crore working-age Indians lack a traditional credit score. Not because they’re risky borrowers, but because they’ve never participated in the formal credit system. The Bank of India MD & CEO has publicly acknowledged that “alternate data sources like payment of utility bills, mobile, telecom bills, UPI channels, e-commerce usage of customers” are being used to sanction loans to new-to-credit customers. The bottleneck was never risk aversion. It was the lack of structured data.

This is where call data enters the picture. Loans below ₹1 lakh have grown 48x since 2017, with NBFCs and fintechs commanding 82% of that market. Traditional banks are largely absent. The lenders winning in this segment are the ones finding ways to structure alternative data, and call data is among the richest, most underused sources available.

But “call data” is a misleading umbrella term. Most articles treat it as a single thing. It’s not. Understanding how to use call data to improve underwriting and credit decision models starts with recognizing that there are three fundamentally different types of call data, each with its own collection method, regulatory treatment, and predictive value.

For a broader view of how AI is transforming financial services, the AI for banking glossary covers foundational terms across the BFSI stack.

The Three Layers of Call Data

This is the framework that no competitor explains clearly. Every credit team working with call data needs to understand these three layers before building anything.

Layer 1: Call Detail Records (CDRs) and Telecom Metadata

Call detail records are data about calls, not from calls. They’re generated by telecom operators and include information like call frequency, call duration, time-of-day patterns, prepaid recharge behavior, and mobile bill payment history.

Academic research consistently shows that CDR-based models outperform traditional lender systems for certain customer segments. A study presented at ICTD '19 in Ahmedabad demonstrated that mobile phone data models delivered higher accuracy across different borrower groups, particularly those with thin or nonexistent credit files. A peer-reviewed study published on PubMed (Hlongwane et al.) confirmed that call detail records and social network behaviors strengthen scoring models and expand credit access.

What CDRs tell a model: Consistency of communication patterns (a proxy for employment stability), recharge regularity (a proxy for income flow), and social network diversity (a proxy for social capital).

What CDRs don’t tell a model: Anything about the content of conversations, the borrower’s intent, or their emotional state.

Layer 2: Conversational Content (Transcripts and NLU Outputs)

This is structured data extracted from actual conversations, typically between a borrower and a lender’s voice agent or call center representative. It includes income declarations, employment details, stated expenses, objection patterns, and responses to KYC verification questions.

Voice AI agents can conduct eligibility screening calls that ask targeted questions (confirming variable income, rental obligations, co-borrower details) and then push structured data directly into a loan origination system. The agents don’t make underwriting decisions. They handle data collection and verification, routing complete applications to human underwriters for credit analysis.

This layer is especially powerful for voice-first KYC and onboarding, where a single call can simultaneously verify identity, collect financial information, and generate structured data points that feed directly into a credit scorecard.

Layer 3: Speech Analytics and Behavioral Signals

This is inferred meaning from how someone speaks, not what they say. Lenders analyze voice interactions to assess creditworthiness through tone, sentiment, speech patterns, confidence levels, and emotional state.

Here’s an important technical nuance: text-based sentiment analysis misses a lot. Meaning is conveyed through tone, pitch, and loudness. Humans provide and receive considerable meta-information during a conversation apart from the words themselves. Speech analytics captures these paralinguistic signals that transcript analysis alone cannot.

For a deeper look at the technology behind this, the conversational analytics software guide explains how speech and conversation analytics differ in practice.

Core Terms Every Credit Team Should Know

Alternative Data

Any non-bureau information used in underwriting. This includes telecom metadata, utility payment histories, e-commerce transaction patterns, UPI usage, rent payments, and data extracted from voice interactions. In the US, two-thirds of financial leaders already use alternative data on new applications, and over half of those leaders reported a 15%+ increase in lending revenue as a result (LexisNexis via defi Solutions). India’s alternative lending market is projected to reach $52.3 billion by 2029.

Credit Decision Model / Credit Scorecard

A statistical model that converts data inputs (features) into a score representing the probability of borrower default. Traditional scorecards rely on bureau data: payment history, outstanding balances, credit utilization. Modern scorecards layer alternative data features on top. The key principle: call data features don’t replace bureau scores. They augment them, especially for thin-file borrowers where bureau data is sparse or absent.

Thin-File / New-to-Credit (NTC) Borrower

A borrower with little or no formal credit history. In India, this is the majority of the working-age population. The reason banks have historically avoided this segment isn’t risk aversion but rather the lack of structured data on NTC customers. Call data, both telecom metadata and structured voice interactions, fills exactly this gap.

For perspectives on reaching underserved borrowers, see this guide on building inclusive financial experiences across regions and cultures.

Psychometric Credit Scoring

A method that assesses creditworthiness based on psychological and behavioral traits rather than financial history alone. It evaluates honesty, reliability, decision-making style, and risk tolerance through structured assessments or digital behavior analysis. Voice AI calls can serve as a delivery mechanism for psychometric-style screening at scale: structured questions, response-time analysis, and behavioral signal extraction, all captured in a single interaction.

Feature Engineering

The process of converting raw call signals into quantifiable inputs that a model can consume. Examples from call data include borrower responsiveness (pickup rates, callback compliance), stated income consistency across multiple calls, sentiment trajectory across collections cycles, and disposition outcomes. This is where the real analytical work happens. Raw call recordings are useless to a model. Engineered features are what create predictive power.

Disposition Outcome

The result code assigned to a call: promise-to-pay, dispute, not reachable, hung up, partial payment committed, or request for callback. Disposition data is the simplest, highest-signal call data type available to most lenders, and it’s almost always underused. More on this below.

Sentiment Analysis

The detection of emotional state from voice interactions. In credit contexts, sentiment analysis tracks borrower confidence, stress, frustration, and cooperation levels. Sentiment trajectory (how a borrower’s emotional state changes across multiple interactions) is particularly predictive for collections outcomes and, by extension, for calibrating origination risk models.

Natural Language Understanding (NLU)

The extraction of structured intent from spoken language. NLU determines what a caller means, not just what they said. In financial calls, NLU identifies intents like “request extension,” “dispute charge,” “confirm employment,” or “decline offer.” Combined with speaker diarization (identifying who said what), NLU transforms unstructured conversations into queryable, model-ready data.

Code-Switching

Language mixing behavior during a conversation, like switching between Hindi and English (Hinglish) or Tamil and English. In credit contexts, code-switching patterns carry demographic and behavioral signals. A borrower’s language choices can indicate education level, urban/rural background, and comfort with financial terminology. Speech recognition accuracy in Indian languages crossed 92% in 2025, making this signal increasingly reliable. For technical details, the code-switching voice AI guide covers how language mixing affects AI accuracy.

Model Retraining / Model Drift

Models decay. AI models trained on pre-COVID borrower behavior produced inaccurate predictions on post-COVID patterns. NBFCs need quarterly retraining cycles with fresh data, a cost that most implementation budgets omit entirely. Practitioners estimate ₹15 to 25 lakh per year per model for ongoing maintenance, monitoring, and retraining.

Consent Architecture

The technical and legal framework ensuring that data collection is purpose-specific, consent-based, and minimal. Under both the RBI Digital Lending Directions 2025 and the DPDP Act 2023, lenders must obtain explicit consent before collecting and processing borrower data. The architecture defines what data is collected, why, how consent is recorded, and when data must be deleted.

How Each Type of Call Data Improves Models

The most overlooked row in this table is the last one. Disposition data from collections calls is structured, immediately available, and highly predictive, yet most lenders treat origination and collections as separate data silos.

For lenders starting to think about AI-assisted collections, structuring disposition outcomes is the fastest path to generating model-ready call data.

Regulatory Boundaries: What’s Allowed and What’s Not

This is where most guides fail. They mention “regulatory compliance” in vague terms without explaining the specific rules. Here’s what actually matters.

The RBI Digital Lending Directions 2025

The RBI is explicit: phone data of the borrower, such as media, files, contact list, call logs, and telephony functions, must not be accessed. Lenders cannot scrape a borrower’s phone to harvest their personal call history.

But there’s a critical distinction that most content overlooks. Lenders absolutely can analyze calls that their own systems initiate and record, provided they have the borrower’s consent. A voice AI agent conducting a KYC verification call, an eligibility screening, or a collections interaction generates data that belongs to the lender’s operational workflow. This data is fair game for analysis and model training, as long as it meets the consent and purpose-limitation requirements.

The DPDP Act, 2023

India’s data protection framework requires that data collection be purpose-specific, consent-based, and minimal. The 2025 RBI Directions align digital lending obligations with the DPDP Act. For call data, this means: collect only what you need, tell the borrower exactly why, store it in India, and delete it when the purpose is fulfilled.

For a detailed breakdown of regulatory alignment, the RBI compliance review covers how voice AI deployments can meet these requirements.

What This Means in Practice

Regulatory compliance is a feature, not a constraint. Lenders who build consent into their voice workflows from day one gain a compliant data advantage. They can analyze every interaction they initiate. Lenders who cut corners on consent can’t use any of it, and face enforcement risk on top.

For US-based readers: an interagency statement from US regulators acknowledged that alternative data “may improve the speed and accuracy of credit decisions” and help “evaluate the creditworthiness of consumers who currently may not have access to credit in the mainstream credit system.” The global regulatory direction is clear. Alternative data is encouraged, but consent and transparency are non-negotiable.

From Raw Calls to Model Features: The Five-Step Pipeline

Understanding how to use call data to improve underwriting and credit decision models requires a concrete pipeline. Here’s how it works in practice.

Step 1: Data Collection

Voice AI agents conduct structured interactions during pre-qualification, KYC verification, eligibility screening, and collections. Each call follows a defined script with branching logic, capturing specific data points at each stage. The agents handle data collection and verification, then route complete applications to human underwriters.

Practitioners on Reddit and builder forums consistently emphasize that the hardest part isn’t the AI logic. After 1,500 outbound AI calls, one builder summarized the pattern as roughly 20% AI logic and 80% orchestration: integrations, daily reports, dashboards, callback scheduling, and edge-case handling. The data collection infrastructure matters more than the model sophistication.

Step 2: Transcription and Structuring

Speech analytics captures, transcribes, and analyzes voice conversations. NLP determines intent and sentiment. Speaker diarization identifies different speakers. The transcript is run against predefined rules to extract structured fields: income stated, employer named, objections raised, commitments made.

Practitioners on Reddit and developer forums consistently point to latency as the number one user-experience complaint in production voice banking deployments. If the transcription pipeline introduces delays, pickup rates and call completion rates drop, which means less data.

Step 3: Feature Engineering

From structured call data, lenders derive quantifiable features:

• Borrower responsiveness: Pickup rates, callback compliance, time to return calls
• Income consistency: Does stated income match across eligibility call, KYC call, and collections interactions?
• Sentiment trajectory: Is the borrower becoming more cooperative or more hostile across the collections cycle?
• Code-switching patterns: Language choices that signal demographic and behavioral attributes
• Disposition patterns: Promise-to-pay rates, dispute frequency, hang-up timing

Each of these becomes a numeric feature that a scorecard can consume.

Step 4: Model Integration

AI models enhance credit underwriting by combining traditional and alternative data, providing deeper insights into borrower risk profiles. Call data features layer on top of bureau scores. For NTC borrowers with no bureau score at all, call data features may be the primary inputs.

The integration point is typically the loan origination system (LOS) or a decisioning engine that pulls features from multiple sources and applies the scorecard. For more on how voice AI connects to banking infrastructure, see the guide on conversational AI in banking.

Step 5: The Feedback Loop

This is the step most lenders skip, and it’s the most valuable. Collections call outcomes contain direct evidence of which borrowers actually repay, which ones dispute, which ones go silent, and which ones negotiate. This data, when fed back into origination models, dramatically improves the accuracy of future lending decisions.

AI-driven collections approaches improve recovery rates by approximately 25% according to TransUnion data. And 60 to 70% of inbound collection calls at Indian NBFCs can now be handled without human intervention. That’s a massive volume of structured disposition data being generated every day, data that should be flowing back into origination scorecards.

Collections practitioners describe this as “the next AI frontier,” where sentiment scoring, route optimization, and one-time settlement computation directly impact recovery rates. The lenders getting this right are treating collections and origination as a single data ecosystem, not two separate departments.

Practical Tips and Common Mistakes

Don’t conflate the three layers. Telecom CDR data, conversational content, and speech analytics are different data types with different collection methods, different regulatory treatments, and different predictive characteristics. Treating them interchangeably leads to confused models and compliance problems.

Don’t skip consent architecture before scaling. It’s tempting to start recording and analyzing calls first and worry about consent later. This is backwards. Under the DPDP Act and RBI Directions, data collected without proper consent is unusable. Build consent into every call flow from the beginning.

Budget for ongoing model maintenance. Plan for ₹15 to 25 lakh per year per model for maintenance, monitoring, and retraining. Most implementation budgets omit this cost entirely. Models that aren’t retrained quarterly will drift, especially in volatile economic conditions.

Start with disposition data. The simplest entry point for using call data to improve underwriting and credit decision models isn’t building a full speech analytics pipeline. It’s tagging and structuring the call outcomes you already have. Every collections call generates a disposition. If you’re not capturing these systematically and feeding them into origination models, you’re leaving the easiest gains on the table.

Account for vernacular complexity. India’s linguistic diversity means that call data carries signals most Western models ignore entirely. Code-switching between Hindi and English, or between a regional language and Hindi, contains real information about borrower demographics and behavior. Models that can’t process multilingual interactions miss these signals.

Don’t overweight speech analytics early. Sentiment and tone analysis are powerful but harder to validate and easier to bias. Start with structured conversational data and disposition outcomes. Layer in speech analytics once you have baseline models and can measure the incremental lift.

Putting It All Together

The opportunity is clear. India’s digital lending market is growing at a 36% CAGR, and the borrowers driving that growth are precisely the ones traditional scoring can’t reach. Using call data to improve underwriting and credit decision models isn’t a theoretical exercise. It’s already happening at NBFCs, fintechs, and small finance banks across the country.

The lenders getting it right share three characteristics. They distinguish between the three layers of call data and use each appropriately. They build consent and compliance into their voice workflows from day one. And they close the feedback loop between collections and origination, treating every call as a data asset that makes the next lending decision smarter.

Book a demo with Awaaz AI to see how millions of voice interactions become structured, model-ready data for credit decisioning.

Frequently Asked Questions

What exactly is “call data” in the context of credit underwriting?

Call data spans three distinct layers: telecom metadata (call detail records showing frequency, duration, and recharge patterns), conversational content (structured information extracted from actual calls, like income declarations and KYC responses), and speech analytics (behavioral signals inferred from tone, sentiment, and speech patterns). Each layer serves different purposes in credit models.

Is it legal to use call data for credit scoring in India?

The RBI Digital Lending Directions 2025 prohibit lenders from scraping call logs, contacts, or media from a borrower’s phone. However, lenders can analyze calls that their own systems initiate and record, provided they obtain the borrower’s explicit consent. The DPDP Act, 2023 requires that all data collection be purpose-specific, consent-based, and minimal.

How does call data help with new-to-credit (NTC) borrowers?

Over 500 million working-age Indians lack a traditional credit score. Call data fills the gap by providing alternative signals: telecom CDRs reveal income stability and consistency patterns, conversational content captures employment and financial details during eligibility calls, and disposition outcomes from collections calls on existing portfolios help calibrate risk models for similar borrower profiles.

What’s the fastest way to start using call data in credit models?

Start with disposition data from your existing collections calls. Every call already generates an outcome (promise-to-pay, dispute, not-reachable, hang-up). Structuring and tagging these outcomes systematically, then feeding them back into origination models, is the lowest-effort, highest-signal starting point. No speech analytics pipeline required.

How often should credit models using call data be retrained?

Quarterly at minimum. Models trained on pre-COVID data produced inaccurate predictions post-COVID. Borrower behavior shifts with economic conditions, and call data patterns shift with it. Budget ₹15 to 25 lakh per year per model for ongoing maintenance, monitoring, and retraining.

Can speech analytics really predict creditworthiness?

Speech analytics adds a behavioral layer that traditional data misses. Tone, sentiment, confidence levels, and emotional trajectories across multiple interactions carry real predictive signal, particularly for collections prioritization and portfolio segmentation. That said, speech analytics works best as a complement to structured data and disposition outcomes, not as a standalone scoring mechanism.

How does the collections-to-origination feedback loop work?

When a collections voice agent records a disposition (promise-to-pay, dispute, payment made), that outcome becomes a data point linked to the borrower’s original loan application data. Over thousands of loans, patterns emerge: borrowers with certain characteristics at origination tend to produce certain disposition patterns in collections. These patterns become features that improve future origination scorecards.

What role does code-switching play in credit models?

Language mixing during calls, such as switching between Hindi and English, carries demographic and behavioral signals. A borrower’s language choices can indicate education level, urban or rural background, and comfort with financial concepts. With speech recognition accuracy in Indian languages exceeding 92% in 2025, these signals are increasingly reliable as model inputs.