Data quality is key for effective ID verification

The cost of living crisis, combined with an increase in digitalisation of services and remote working at a time of great volatility in the world, has created new motives and justification for fraud to flourish. In fact, the current turmoil has fed into the three elements of the fraud triangle: motivation, opportunity and rationalisation. As a result, it’s widely anticipated that fraudulent activity is set to grow globally.

Clean data powers ID verification

While using ID verification tools is vital to meet AML and KYC regulations and prevent fraud - particularly in these challenging times - the cost of using such tools can be reduced and fraud will much less likely to occur if there are ongoing processes in place to ensure data hygiene.  

For example, address validation, for proof of address, is about matching a name to an address, and is a crucial part of the data hygiene process.

Because ID checks will pick up rudimentary things, such as an incorrectly formatted address, it’s much better value and best practice to ensure that you have accurate user contact data in the first place, before undertaking identity verification. 

Having accurate data is not only important for ID verification. Once you have clean data on customers you can obtain a single customer view (SCV). With this insight it’s possible to improve targeting, including personalisation with communications.

Unfortunately, 91 per cent of organisations have common data quality problems, with user contact data degrading at 25 per cent a year without regular intervention. Also, 20 per cent of addresses entered online contain errors, such as spelling mistakes, wrong house numbers and incorrect postcodes.

The good news is incorrect contact data, such as name, address, email or telephone number are straightforward to fix with simple and cost-effective changes as part of a data quality regime. This should involve cleansing and standardising held user data to deliver data quality in batch, as well as when new data is collected, in real-time. Also, the cleansing tools used in this process should be able to enhance the data by filling in any missing contact details.

Four steps to ensure clean customer databases:

1)     Use an address autocomplete tool or lookup service. These are vital when it comes to ensuring accurate data is collected at the onboarding stage, during a period when many people are completing contact forms on small mobile screens where they are more liable to make mistakes. They also enable financial institutions to deliver a standout service by reducing the number of keystrokes required—by up to 81 per cent—when typing an address. This speeds up the onboarding process, reducing the probability of the consumer not completing an application or making a purchase. These tools also support ID verification because they deliver address validation.

2)     Deduplicate data. Duplicate data can be an issue for those in financial services, usually caused when two departments merge their data and by mistakes in contact data collection at different touchpoints. This adds cost in terms of time and money, particularly with duplicate printed communications, which can also adversely impact on the sender’s reputation. The recipients will see this as a waste of money, and assume the sender is not concerned about the environment. To prevent this, source an advanced fuzzy matching tool to deduplicate data. By using such a service, and merging and purging the most difficult records, you save money with communications and improve the user experience. Additionally, the potential for fraud is reduced because a unified record is established for every customer.

3)     Data suppression tools are critical for highlighting customers who have moved or are otherwise no longer at the address on file. As well as removing bad addresses which could be used by fraudsters, services that include deceased flagging ensure mail and other communications are not sent to those who have passed away, upsetting their friends and relatives. It’s not only ethical to use these suppression strategies, but it also helps financial institutions to save money and protect their reputation.

4)     Use a data quality management platform. With evolving technology it’s even more straightforward to deliver data quality in real-time and cost effectively. Today, you can pay for a licence to access a scalable data cleaning software as a service (SaaS) platform that requires no code, integration or training - simply plug in and benefit, immediately. Using a single, intuitive interface these tools can provide data standardisation, validation, and enrichment to deliver high quality contact information across multiple databases.

Utilising ID verification tools, for example electronic identity verification (eIDV) and those that use biometric technology to prevent fraud and meet AML and KYC regulations, will always be essential. However, it’s best practice to deliver ongoing data hygiene. It’s only by having clean data that you can effectively, and at low cost, undertake identity verification, and reap the additional benefits of having accurate customer data, such as improving targeting and delivering a better experience for those who use your services and buy your products.