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IFFCO-Tokio Saves Over $1M Annually on Fraud with

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Over $1M USD / 70M INR Annual savings on the fraud use case



100x Confirmed fraudulent claims detected each month



3% Customer retention increase within 1 month

In the insurance business, one of the biggest challenges is dealing with fraudulent claims, which may be difficult to detect, resulting in financial loss. IFFCO-Tokio General Insurance Company Limited decided to turn to AI for help in detecting potential fraud cases involving motor and health claims. With solutions and models built using the H2O AI Cloud platform, it has managed to sieve out fraudulent claims and expects to save almost 70 million Indian Rupees annually (US$1 million).

Incorporated in 2000, IFFCO-Tokio is a joint venture between Indian Farmers Fertilizer Co-operative Limited, the world’s biggest co-operative reaching more than 50 million Indian farmers, and Tokio Marine Group, the largest property and casualty insurance group in Japan. It offers a wide range of insurance products to 10 million customers across India




As one of the biggest motor insurance companies in the world’s second most populous country, IFFCO-Tokio receives more than 2,000 motor claims every day that are processed across 12 claim service centers by around 100 claim officers, within specified timelines. The process is not only time-consuming, but also demanding. As a result, claim officers often do not have the time to devote much attention to every case to check against possible fraud.

Motor insurance fraud can range from providing misleading details and inflating repair costs, to staging accidents and claiming for fictitious injuries or losses.

IFFCO-Tokio wanted to tap the power of AI to analyse incoming claims and spot anomalies to reduce fraudulent claims that could hurt its bottom line. That same solution can then be adapted to other products, such as health insurance, which face similar challenges.




The claim fraud prediction solution that it envisaged must be able to detect fraudulent activity, suspicious links and behavioral patterns based on historical claims data. “We were delighted when we came across whose Kaggle Grandmaster guided my team remotely on using’s automated machine learning with pre-built algorithms,” said Seema Gaur, Chief Information Officer and Head of IT of IFFCO-Tokio. “We could deploy it on-premise and use it with a small team, which is ideal for our small team of 3 developers who have only a couple of years of experience. Deploying it on premise is vital for our business due to data privacy,” she added.

Using the platform, the model gives each claim a score of between zero and one, with one being a higher probability of fraud. With the system flagging such cases instantly and with greater accuracy, the team can pay greater attention to claims with higher scores. Following the successful deployment of the fraud prediction model for motor insurance claims in July 2021, the company then created another model to analyze, detect and predict health insurance claims.

Unlike motor insurance claims, which are done by the policyholder, health insurance claims in India involve a third-party administrator acting as an intermediary between the hospital, which is claiming for services provided, and the insurance company. In most cases, payment is made directly to hospitals.

From the moment a patient is admitted to the point of discharge, there are many triggering points for fraud ranging from medically unnecessary services to overcharging for over-prescription of medicine. A combination of rules and data from a third party is used to train the model to detect such frauds.




IFFCO-Tokio is reaping benefits from using the AI models. Based on more than 100 confirmed fraudulent claims detected each month so far, the projected saving from fake motor and health insurance claims are around US$1 million annually

Additionally, the insurance company’s claims officers can now focus on following up on possible fraud cases flagged by the system instead of analyzing every single claim, resulting in faster processing time for genuine claims

In another development, IFFCO-Tokio is also training an AI model developed using to determine the propensity for customer retention or the likelihood of customers to renew policies.

“Customer retention is vital for our business success. We want to know which customers are more likely to renew so that we can follow up to get them signed up quickly,” said Gaur.

Since the start of the trial, involving their online customer base, in July 2021, customer retention has increased by three percent.


Customer success partnership


“H2O AI Cloud is a platform that my team can learn by themselves. I find the platform to be very friendly for people who are not data scientists or have little knowledge of data science. I also appreciate the team for their responsiveness and willingness to help our team of non-AI trained professionals. They willingly share their knowledge to help our team to grow our capabilities,” Gaur said.

Gaur’s experience with and the success that she has enjoyed with the AI models have inspired her to go further and pursue other use cases, including image analysis for pre-inspection of used vehicles, and to assess motor insurance applications and claims.

“This is the beginning of the usage of AI and machine learning for our company. We hope to make more use of it in the long run,” said Gaur.

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