Quality data is required to maximise AI efforts

The power of artificial intelligence (AI) is proving transformative in financial services, from enhancing data-driven insight and productivity, to improving fraud detection and the customer experience.

The opinions expressed here are those of the authors. They do not necessarily reflect the views or positions of UK Finance or its members.

However, issues occur when inaccurate customer data is fed into AI tools. For example, if used to prompt or fine tune a large language model, this could increase the risk of ‘hallucinations’, which leads to poor results.

Data decays swiftly

Data deteriorates fast with customer contact data lacking regular intervention degrading at 25 per cent a year as people move home, die and get divorced. Additionally, 20 per cent of addresses entered online have errors; these include spelling mistakes, wrong house numbers, and incorrect postcodes.

To avoid this, and maximise your AI efforts, it’s important to have verification processes in place at the point of data capture, and when cleaning held data in batch. This typically involves simple and cost-effective changes to the data quality process. 

Address autocomplete / lookup for accurate data in real-time

Address autocomplete or lookup services are valuable pieces of technology to use at the customer onboarding stage. They provide accurate address data in real-time when onboarding new customers by delivering a properly formatted, correct address when they commence inputting theirs. They also reduce the number of keystrokes required, by up to 81 per cent, when typing an address. This speeds up the entire onboarding process, reducing the probability of the user not completing an application to access a service, for example. This approach to first point of contact verification can be extended to email and phone, so that these important contact data channels can also be verified in real-time. 

Access a data cleaning SaaS platform 

Delivering data quality in real-time to support AI and wider business efficiencies has never been easier or more cost-effective. It’s possible to obtain a scalable data cleaning software-as-a-service (SaaS) platform that doesn’t require any coding, integration, or training. This technology cleanses and corrects names, addresses, email addresses, and telephone numbers worldwide. It matches records in real-time, ensuring no duplication, and offers data profiling to help source issues for further action. A single, intuitive interface provides the opportunity for data standardisation, validation, and enrichment, resulting in high-quality contact information across multiple databases. This activity can take place with data held in batch and as new data is being collected, and can also be accessed via cloud API or on-premise, if preferred

AI can add value and a competitive edge for those in financial services, but this is reliant on the quality of data fed into the AI tools. Inaccurate data leads to the ‘garbage in, garbage out’ risk of AI inaccuracy – or even AI ‘hallucinations’ – with unreliable predictions and bad outcomes. Applying best practice data quality procedures solves this issue and helps to maximise the success of your AI efforts.