86 Lakh Policies Wiped Out: India’s Life Insurance Lapse and Persistency Data (FY 2024-25)

Every year, lakhs of Indian families lose life insurance coverage they already paid for. They don’t lose it to claim rejection or insurer failure. They lose it because a premium payment got missed, a bank mandate expired, or a policy sold at a branch counter was never really wanted in the first place.

The technical term is “lapse.” The practical consequence is that a family that thought it had financial protection no longer does. And the scale of this problem is staggering. In FY 2024-25 alone, approximately 86 lakh individual non-linked policies lapsed across all life insurers in India, erasing ₹8.7 lakh crore in sum assured. That is coverage that existed on paper one day and was gone the next.

This article uses twelve years of IRDAI lapse and persistency data to show you which insurers lose the most policyholders, which have turned their numbers around, and what the pattern of policy abandonment tells you about mis-selling in India’s life insurance market. If you hold a life insurance policy (or are about to buy one), the numbers here should change how you evaluate your insurer.

The scale of India’s lapse problem

The industry-wide numbers have improved slightly from their peak in FY 2020-21, when over 95.8 lakh policies lapsed. But the absolute scale remains enormous, and the sum assured forfeited has climbed as policy sizes grow.

The FY 2023-24 dip to 75.3 lakh was encouraging, but FY 2024-25 reversed it with a jump back to 86 lakh. The sum assured column tells a separate story. Despite fewer policies lapsing in FY 2023-24, the coverage forfeited was the highest in the table at ₹9.87 lakh crore. Newer policies tend to carry higher sum assured, so each lapse now destroys more coverage than it did five years ago.

LIC accounts for the majority of lapsed policies by sheer volume. In FY 2024-25, 68.6 lakh LIC policies lapsed, worth ₹5.84 lakh crore in sum assured. LIC’s lapse ratio (2.55%) is among the lowest in the industry, but when your in-force book runs into crores of policies, even a small percentage translates to enormous absolute numbers.

Which insurers lose the most policyholders?

Lapse ratio measures lapsed policies as a percentage of total policies in force. It is the fairest way to compare insurers of different sizes. Here is how every major insurer has performed over the past six years.

Shriram Life is in a league of its own. At 12.6%, its lapse ratio is nearly triple the next-worst insurer. The company has improved from its FY 2021-22 peak of 19.5%, but it still loses roughly one in eight policies every year. Given that Shriram also has the worst persistency numbers (more on this below), the pattern points to a structural problem in how its policies are sold and serviced.

At the other end, LIC, Reliance Life (2.2%), and Ageas Federal (2.2%) hold the lowest ratios. But low ratios don’t always mean small damage. HDFC Life’s 3.3% translates to 2.03 lakh policies worth ₹47,043 crore. ICICI Prudential at 3.5% lost 1.24 lakh policies worth ₹36,962 crore. Max Life at 2.9% lost 1.31 lakh policies worth ₹49,946 crore. When you’re dealing with high-value term and savings policies, a “low” lapse ratio can still wipe out tens of thousands of crore in coverage.

The turnaround stories

Not every trend line is grim. Several insurers have dramatically reduced their lapse ratios over the past decade, and the improvements are large enough to rule out statistical noise.

Aditya Birla Sun Life had the most dramatic turnaround. In FY 2013-14, nearly half of all its in-force policies lapsed. By FY 2024-25, that figure was 3.84%. The trajectory was steady: 34.6% in FY 2014-15, 22.8% in FY 2015-16, 11.2% in FY 2018-19, and then single digits from FY 2019-20 onward. This kind of sustained improvement typically reflects a wholesale change in distribution quality and underwriting discipline, not just a market cycle.

ICICI Prudential followed a similar arc, from 26.87% in FY 2013-14 to 3.53% in FY 2024-25. The steepest part of the improvement came between FY 2015-16 (13.9%) and FY 2016-17 (6.1%), when the company restructured its agency model. The ratio has continued to tighten every year since.

Bharti AXA dropped from 21.6% in FY 2019-20 to 4.3% in FY 2024-25. IndiaFirst cut from 10% to 2.8% over the same period. These are genuine improvements that indicate better product-market fit and more honest selling.

The exceptions stand out. Bajaj Allianz Life, which had reached an excellent 2.6% in FY 2019-20, has crept back up to 4.4% in FY 2024-25. Star Union Dai-ichi dropped from 11.5% to 4.7% between FY 2019-20 and FY 2022-23, then jumped back to 7.2% in FY 2023-24 (settling at 6.4% in FY 2024-25). Turnarounds are not always permanent.

The persistency decay curve

Lapse ratio tells you what happened in a single year. Persistency tells you how a cohort of policies survives over time. IRDAI measures it at five checkpoints: the 13th, 25th, 37th, 49th, and 61st month from policy inception. The 61st-month figure is the most telling; it answers the question “what fraction of policies sold five years ago are still in force today?”

Here is the FY 2024-25 persistency curve for eight insurers that represent the range from best to worst.

The gap between top and bottom is enormous. At ICICI Prudential, 58.8% of policies survive five years. At Bharti AXA, 22.2% do. That means nearly 4 out of 5 Bharti AXA policyholders who bought a policy five years ago are no longer covered.

The decay is steepest early. Across all 22 insurers with full FY 2024-25 data, the average drop between the 13th and 25th month is 10.5 percentage points. The drops between subsequent checkpoints are smaller: 4.6 pp from month 25 to 37, 5.5 pp from month 37 to 49, and 5.7 pp from month 49 to 61. The first renewal window is where most policies die.

For individual insurers, the decay curve shapes vary. HDFC Life loses 10.7 pp at first renewal (month 13 to 25) but only 2.9 pp between months 25 and 37. Shriram Life drops a staggering 23.8 pp between month 13 and month 25; more than a third of surviving policies vanish at the first renewal. Tata AIA and Max Life show a more gradual, even decline across all intervals.

What your insurer’s persistency says about mis-selling

A high lapse ratio combined with low persistency is the statistical fingerprint of mis-selling. When a policy is sold to someone who doesn’t fully understand what they’re buying (or didn’t really want it), the first premium renewal is where reality catches up. The policyholder sees the debit, realizes the policy isn’t what they expected, and stops paying.

This pattern shows up most clearly in bank-sold insurance (bancassurance). When you buy a policy at a bank branch, the person selling it to you is primarily a banking relationship manager, not an insurance advisor. The incentive structure pushes product bundling; the understanding of whether the policy fits the customer’s needs often comes second.

Star Union Dai-ichi, the joint venture between Bank of India and Union Bank of India, has a 61st-month persistency of just 28.8%. Over 70% of policies sold five years ago are gone. IndiaFirst (Bank of Baroda) sits at 42.4%. These are companies where the bulk of sales happen at bank counters, and the persistency data suggests a significant portion of those sales don’t reflect genuine customer intent.

The non-bank insurers cluster between 52% and 59% on 61st-month persistency. That is still not great (it means roughly half the policies don’t survive), but the gap with the weakest bank-affiliated players is 25 to 30 percentage points. The pattern is consistent across years, not a one-off.

Canara HSBC Life is the exception that proves the rule. Despite being a bancassurance insurer, it has improved its 61st-month persistency from 37.0% in FY 2014-15 to 57.1% in FY 2024-25. It’s proof that being bank-affiliated doesn’t automatically mean bad persistency; it depends on how the bank channel is managed. Canara HSBC’s lapse ratio (5.4%) is still higher than the non-bank average, though, so there’s room left to close.

If you want to understand how India’s insurance industry handles claims once a policy does stay active, see our claim settlement ratio ranking. For cases where claim disputes end up in court, the NCDRC court case analysis shows the most common reasons for rejection. And the COVID death claims analysis reveals how different insurers handled the pandemic surge.

What this means for you

If you’re shopping for term insurance, persistency data is one of the most underused tools available. Here’s how to use it.

Check the 61st-month persistency before buying. A low number means that a large fraction of people who bought from that insurer stopped paying within five years. That can signal aggressive selling, poor product design, or inadequate customer service. It doesn’t guarantee you’ll have a bad experience, but it’s a red flag. An insurer where fewer than 40% of policies survive five years should prompt extra scrutiny.

Be wary of policies sold at bank branches. If your bank relationship manager suggests a life insurance policy, especially one bundled with a loan or deposit, take the brochure home and compare it against direct-purchase options from the same insurer. The bank channel is where the most mis-selling happens, and the persistency data confirms this year after year.

Set up auto-debit from day one. A large portion of lapses happen not from deliberate cancellation but from missed payments. A standing instruction on your bank account or a credit card auto-debit eliminates this risk. If your insurer doesn’t support auto-debit (some smaller ones don’t), set a calendar reminder 30 days before your premium due date.

Know your revival window. If your policy has already lapsed, you may be able to revive it. Most insurers allow revival within five years of lapse, subject to paying outstanding premiums with interest and sometimes a fresh medical exam. Our lapsed policy revival guide walks through the process step by step. Revival is almost always cheaper than buying a new policy at your current age.

Look at lapse trends, not just current ratios. An insurer whose lapse ratio is falling steadily (like ICICI Prudential, from 9.6% to 3.5% over six years) is likely improving its sales and servicing practices. One whose ratio is rising (like Bajaj Allianz, from 2.6% to 4.4%) may be scaling distribution faster than quality controls can keep up.

Frequently asked questions

Methodology

All lapse data in this article comes from Table 27 (“Details of Forfeiture/Lapsed Policies, Individual Non-Linked”) of the IRDAI Handbook on Indian Insurance Statistics for the years 2022-23, 2023-24, and 2024-25. Persistency data comes from Table 28 (“Persistency of Life Insurance Policies, Based on Number of Policies”) of the same handbooks. Industry-wide totals were computed by summing insurer-level figures for each financial year. Lapse ratios are based on number of policies, not premium or sum assured. Persistency is measured based on number of policies at the 13th, 25th, 37th, 49th, and 61st month from policy inception. The year label “2025” in persistency data corresponds to FY 2024-25 (April 2024 to March 2025). All figures are for individual non-linked policies only; group and unit-linked policies follow different IRDAI reporting tables.

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