Tale of Data use cases in the insurance industry
The escheatment of bank accounts and life insurance policies is a technically complex subject for a number of banks and insurance companies. Some of them have recently been heavily sanctioned by the ACPR.
Under the Eckert Act, banks and insurance companies are required to draw up a list of dormant accounts and policies. This implies that the relevant departments of these establishments must seek out and inform the holders and beneficiaries within the legal deadlines.
In practice, however, this search is not very fruitful. In order to maximize the chances of cross-referencing, it is essential to have high-quality, up-to-date data. And it's here that the research work really gets tough. The key problem is to process the data appropriately to obtain reliable results.
The phenomenon of escheated contracts is proving to be of greater and longer-lasting magnitude than initially estimated when the Eckert law was passed in 2016. As a reminder, at the end of 2017, Caisse des Dépôts Consignations (CDC) had €4.7 billion in unpaid escheated contracts.
Unclaimed assets are transferred to CDC by banks and insurance companies after a certain period of time.
When no transactions have been carried out on a bank account for twelve consecutive months and the account holder has not contacted the bank, or when the account holder has died and the beneficiaries have not asserted their rights within a period of one year following the death, the bank or insurance contract falls into the escheat phase. Normally, banks and insurers are obliged to keep escheated contracts in their management systems for several years. During this period, they are obliged to find the policyholder or beneficiaries. The period is five years for savings books, term accounts, securities accounts and PEEs (company savings plans). The limitation period for life insurance contracts is ten years. Once this period has elapsed, the funds from escheated contracts are transferred to the Caisse des Dépôts et Consignations. The latter must then keep them until the end of the thirty-year prescription period. At the end of thirty years, if no claim has been made by the policyholder or his heirs, the unpaid funds are definitively transferred to the French State.
Lapsed contracts are assets that have not been claimed, or that have not been paid out to the policyholder or beneficiaries upon the policyholder's death. There are a number of reasons for these unclaimed contracts:
The policyholder has died and the policyholder's heirs have not contacted the bank or insurance company to obtain payment of the capital.
The policy has expired and the policyholder has not requested payment of the capital sum.
The policyholder has died and his or her contact details are incorrect or missing: the insurance company cannot pay out the policy capital.
It should be noted that the bank or insurance company is not always informed of the policyholder's death.
In the first instance, it is up to the policyholder's family and friends to inform the bank or insurance company in the event of his or her death.
Secondly, banks and insurance companies are responsible for identifying deceased customers.
Data quality is at the heart of the problem of escheated contracts. To successfully cross-reference data, it is important to have relevant, exhaustive and accurate data. From the outset, the information in the management systems of banks and insurance companies must be corrected.
Very often, the most widespread types of error are of a spelling nature, due to typing errors for example (inversion of two letters, acronyms, inadvertent or deliberate misspellings, etc.). A name can be written in two different ways, but the similarity of the two spellings is difficult for a computer to manage. There are also other types of data that cause cross-referencing problems. These include address abbreviations (av. for avenue) and dates (87 for 1987).
Covering a wider perimeter, or proposing corrections for all fields such as surnames, first names and addresses, enables a balance to be struck between the quality of the corrections made and the number of data items to be cross-referenced.
To achieve this, it is necessary to set up a system of algorithms capable of performing the join between two data tables by reconciling several flow columns. It is even worth developing a fuzzy matching algorithm directly from the RNIPP database.
Generally speaking, the phonetic algorithm and the measurement algorithm are two approaches that can identify various errors (name variants, name inversion, insertion or deletion of punctuation, spaces, special characters, different spelling of names, e.g. 'Jon' for 'John', etc.).
Data quality within banking and insurance management systems is essential to optimize the process of identifying the beneficiaries of escheated policies.
Non-application of the Eckert law is severely punished by the ACPR. At the end of May 2022, the bank Natixis Interépargne received a pecuniary penalty of 3 million euros from the regulator for its faulty recognition system. In fact, it had used the Insee death database, whose history ended in 2014. Nor did it cross-reference with the RNIPP database. It also largely reactivated inactive accounts by mistake. This was based on the return of letters sent to account holders as undelivered mail.
A month earlier, the ACPR had imposed a record fine of 8 million euros on the insurance company Mutex. The regulator criticized the company for a series of failings in its management of retirement contracts, in particular the lack of action in the search for beneficiaries of escheated contracts. The insurer put in place a number of tools, but failed to address these issues.
To avoid this kind of costly malfunction, Tale of Data has developed a solution for making corrections to improve the quality of raw data. Its application enables names to be reconciled using the phonetic algorithm, and special or unnecessary characters such as "né", "épouse", etc. to be removed. Unwanted punctuation or spaces can also distort your search. Our tool eliminates these errors and standardizes your data, whatever the format, typology or size. In fact, this new-generation solution, integrated with our Tale of Data software, outperforms other competitive solutions.
Thanks to the data cleansing operation, our algorithm can then perform cross-referencing operations and facilitate data integration.
This process is ideal for matching the names of people in your database with other internal or external databases, by comparing approximate matches. Our matching tool makes it possible to match information based on pre-defined parameters, which can be weighted according to the level of interest.
To demonstrate the effectiveness of our solution, here's a typical example of a recurring problem in the search for the owners or beneficiaries of escheated policies. In one of your files, you have a person named Mrs. Malorie Jullien-Dunes. However, she doesn't exist anywhere else. With our tool, the algorithm will look for approximate matches and suggest several: Malaurie Jullien-Dunès and Malaurie Julien. Other criteria will enable you to refine these results by comparing date of birth, names of beneficiaries if any, address, etc.
For more information, you can send us some data for a test with our solution.