At the point of application, a lender knows exactly who they are dealing with. The borrower's mobile number is verified by OTP. The address is checked against utility records or an Aadhaar-linked database. The employer details are confirmed by a salary slip or an HRMS connection. The contact data is accurate, verified, and stored. 

Six months later, some of it is wrong. Twelve months later, more of it is wrong. Twenty-four months into a 36-month personal loan, a meaningful portion of the borrower contact data the lender is relying on for collections, servicing, and communications is describing a person who has partially moved on - to a different number, a different address, a different employer, a different city.

This is contact data decay. It is not dramatic. 

It does not happen all at once. It is a gradual, continuous divergence between the static record the lender holds and the dynamic reality of the borrower's life - and it is one of the most consistently underestimated risks in retail lending portfolio management. 

Understanding the decay curve

Contact data does not decay uniformly. Different contact point types decay at different rates, and the decay rate in India is significantly higher than in most comparable markets because of specific structural factors that accelerate it.

Mobile numbers decay fastest. India's MNP volumes - over 13 million porting requests in a single month as of December 2024 - reflect a telecom market where switching is easy, frequent, and often triggered by price changes that affect the entire subscriber base simultaneously. 

Beyond porting, numbers are abandoned when prepaid SIMs go inactive, when borrowers acquire secondary SIMs for personal use that become primary over time, or when numbers registered in a relative's name at origination are no longer the borrower's point of first contact. Industry estimates suggest mobile number accuracy in retail lending portfolios degrades measurably within six months and significantly within twelve.

Residential addresses decay slower but with higher consequences when they fail. India's workforce mobility - rural-to-urban migration, inter-city job transfers, the movement patterns of young salaried workers through shared accommodation - produces address changes at rates that are rarely reflected in lender records because there is no mechanism to capture them between origination and a collections event. An address that was accurate at disbursement may describe a previous city of residence by the time a legal notice needs to be served at it.

Employer contact details decay with job tenure. India's salaried workforce changes jobs at rates that are high relative to the tenure of most retail loan products. A 36-month personal loan will see a proportion of its borrowers change employers at least once during the loan period - taking with them the HR contact, the office address, and the employer email the lender captured at origination. 

When the employer reference needs to be contacted, the lender is reaching an organisation that no longer has any relationship with the borrower.

Why one-time KYC verification is structurally inadequate

The instinct in most lending institutions is to treat contact verification as a KYC function - something that happens once, at onboarding, and is then complete. This instinct is understandable but structurally wrong for the problem it is trying to solve.

KYC verification at origination confirms that the contact data provided is accurate at that moment. It does not confirm that it will remain accurate. For a two or three-year loan product, the relevant question is not "was this number correct on the day the loan was approved?" 

It is "is this number correct today, at the moment we are trying to reach this borrower?" These are entirely different questions, and one-time KYC answers only the first of them.

The gap between what one-time KYC provides and what collections operations actually need is a dynamic, continuously maintained view of borrower contact data - one that reflects how that data changes over the life of the loan, not just how it looked on origination day.

This is not a novel insight. Collections teams in retail lending have understood this problem for years. What has been missing is a scalable, automated mechanism to address it - one that does not require a manual skip tracing exercise every time a contact point fails, and that does not wait for a failed contact attempt to signal that the data has become stale.

The cost distribution across the portfolio

Contact data decay does not affect all accounts equally, and understanding the distribution of the problem helps prioritise where to address it first.

The accounts most vulnerable to contact data decay are those with long remaining tenures combined with high borrower mobility - typically younger salaried borrowers in urban centres, where job switching, SIM churning, and residential mobility are all higher than average. A 28-year-old software engineer who took a 36-month personal loan is a borrower profile where the origination contact data has a high probability of being at least partially stale by month 18.

The accounts where stale contact data is most expensive are those in early delinquency - the first bucket, where right-party contact rate is the primary determinant of resolution speed and cost. An account that enters Day 1 Past Due and cannot be reached because the registered number is inactive is an account that will age. The difference in resolution cost between a Day 10 resolution and a Day 60 resolution - driven by contact failure at the early stage - is substantial and well-documented in collections operations data.

The accounts where stale contact data creates the most severe consequences are those that reach legal action with an unverifiable address. Legal proceedings require service to a verified current address. When the address on file is outdated, the proceedings stall while current address investigation is conducted. The delay adds cost, extends timelines, and in some cases allows the asset to depreciate or the borrower's financial position to deteriorate further during the extended process. 

This is the same underlying exposure covered in how NBFCs can close the field verification gap: an unreachable borrower at the point it matters most is rarely a sudden event, it is the endpoint of months of undetected drift.

What continuous contact verification looks like in practice

The alternative to one-time KYC verification is continuous contact point verification - a systematic approach that maintains the accuracy of borrower contact data throughout the loan lifecycle rather than only at origination.

In practice, this operates at two levels. The first is proactive periodic verification - automated checks against live telco, address, and employment databases at defined intervals across the loan lifecycle. A check at six months, at twelve months, and at the point of any missed payment confirmation that the contact data on file is still active and reachable. When it is not, the system flags the discrepancy and initiates a data refresh before the contact point is needed for a critical communication.

The second level is triggered verification - automatic contact data checks initiated by specific events in the borrower's relationship with the lender. A failed OTP delivery triggers an immediate number verification check. A returned statement triggers an address verification check. A declined EMI triggers a full contact refresh before the collections sequence begins. 

The verification is demand-driven rather than scheduled, which means it catches data decay at the moment it becomes operationally relevant rather than on an arbitrary periodic cycle.

The output of continuous contact verification is not just refreshed data. It is a collections operation that is structurally faster and more efficient than one relying on static origination data. Right-party contact rates improve because numbers and addresses are confirmed before they are used. 

OTP success rates improve because stale numbers are identified before authentication is attempted. Legal proceedings are served to current addresses because address verification is a live process rather than a one-time capture.

The regulatory dimension

There is a compliance dimension to continuous contact verification that is becoming more significant as RBI tightens its expectations around collections practices.

The RBI's Digital Lending Directions 2025 require that collections communications be made to verified, consent-backed contact points - not to any number the lender happens to have on file. 

Attempting to reach a borrower through an unverified contact point, or through a contact point whose associated consent was not specifically obtained for collections purposes, is a potential Fair Practices Code violation. With ₹48 crore in aggregate FPC penalties levied in FY 2024-25 alone, this is not a theoretical risk.

Continuous contact verification provides a natural compliance safeguard. When every contact point is verified before use - confirmed as active, confirmed as associated with the borrower, confirmed as covered by the relevant consent - the lender's collections operation is on solid regulatory ground regardless of which contact it uses. 

When contact points are used as-captured at origination, months or years later, without verification, the lender is relying on data whose accuracy it cannot confirm and whose consent coverage may have been superseded by subsequent communications preferences.

Where Tartan's DCPV sits in this workflow

Tartan's Digital Contact Point Verification is built for exactly this use case - not one-time KYC verification at origination, but continuous, real-time contact data verification across the lending and collections lifecycle.

DCPV cross-references borrower contact data against live telco, address, and employment sources, confirming which contact points are current and reachable, flagging those that are not, and surfacing verified alternatives where they exist.

For collections operations specifically, DCPV integrates into the workflow at the point of use - before a call sequence begins, before an OTP is sent, before a legal notice is prepared. The collections team works from verified contact data, not from static origination records that may be months out of date. The right-party contact rate improves not because the team is working harder, but because the data they are working from is accurate.

Contact data decay is a certainty in any retail lending portfolio in India. The question is not whether the person who applied six months ago is still reachable at the same number and address - statistically, a growing proportion of them are not. The question is whether the lender finds this out proactively, before a critical communication fails, or reactively, after a missed payment has already aged past the point of easy resolution.

The gap between who applied and who you can reach today is measurable. In most portfolios, it is larger than collections operations have formally quantified. And it is entirely addressable - not with a process improvement, but with a data infrastructure decision that keeps borrower contact data as current as the origination data was on day one.