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Published on
Updated on
December 6, 2024

Uncovering Hidden Risks

[ Summary ]

A reinsurance company leveraged Lazarus AI to identify critical data anomalies during a potential acquisition. By integrating seamlessly with existing tools, Lazarus AI revealed significant under-reserving issues, saving the company from unforeseen liabilities. This case study highlights the power of AI in enhancing due diligence and ensuring data quality, providing a competitive edge in the insurance industry.

Industry problem  

Insurers, Reinsurers, Actuarial Consultants and Investment Bankers all have to price blocks of business from a valuation perspective. Accuracy becomes especially critical in discussion of an acquisition or divestiture, as multiple parties will be examining and looking for information anomalies that can lead to pricing advantages. In addition to the large number of actuarial and valuation tools that the industry has, experts involved in acquisitions and divestitures will have additional tools that augment the traditional industry tools. All of these tools, standard and augmented, largely have to take the data input as a given and perform analysis from there.

The opportunity

Using AI to examine the input data for anomalies will allow existing tools to continue to be used but whoever can get insights into the data quality received will have knowledge arbitrage over those who have to accept data as a given input analogous to a "black box". The entire "Due Diligence" process is critical and undertaken with greatest import by those involved. However, there is a reality that there are time pressures. Even if there is an ability to review all data manually, which is highly unlikely, these time pressures  increase the probability that people might miss a non-obvious pattern.

Specific client example

A reinsurance company that specializes in providing liquidity and risk transfer solutions to the insurance sector had exactly this problem. That is, the company was very experienced at valuation and had all the traditional tools but wanted deeper insights. That is, this company wanted to challenge the current constraint where all inbound data is a "black box,"  taken as a given. The company was considering acquisition of a block of business and executing the traditional valuation approaches. In parallel, they decided to use Lazarus AI to examine the block of business for data anomalies.

Lazarus AI Solution:

Lazarus AI brought one of  our proprietary business trained models to this problem.  Without further training, the model was fed all of the data for valuation that was available for the block in question. Although model training was not required, Lazarus did support the process by providing prompt engineering expertise. Due to Lazarus AI ability to integrate into existing technology set, there was no need to "rip and replace" or even to modify existing technologies.

Value realized

During this process, a situation was identified by Lazarus AI proprietary models that caused additional questions. Upon reviewing the data, a significant under-reserving issue was discovered. The Insurer made the decision to not proceed further based upon what was uncovered. Total run time of 17 hours with all output validated in 48 hours. The insurer found the time and dollar investment well worth avoiding an acquisition with previously unforeseen liabilities.

Executive Summary

Traditional pricing and valuation models have inherent blindness about the data quality being used as input, in essence: sampling risk. Lazarus AI can bring light into those situations. Lazarus AI does not require a "rip and replace" approach but allows a ride along ability for enlightenment alongside existing tools.

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