Your AI plans are dead in the water without the right data to fuel them

By Steve Round, Founder, SaaScada

Artificial Intelligence (AI), and in particular generative AI, has been a hot topic for a while now, but it seems that sometimes we lose sight of the fact that AI is only as good as the data that feeds it. The first recorded use of the term Garbage in, Garbage out (GIGO) was in 1957. While things have changed a lot since then, the term is still as relevant today as it was then – possibly more so. Especially with generative AI mimicking human creativity, getting the full data set to inform the algorithm is essential – who hasn’t had to have a few cracks at providing appropriate data to get the content they wanted out of ChatGPT, Bard, Alphacode, etc?

Feeding the machine 

In the rapidly evolving landscape of financial services, the integration of AI will become pivotal for institutions seeking to stay competitive and provide enhanced services. But how many banks have the data they need? Even for banks that do have a wealth of data, how much of it is stored in product or department silos? How much has been lost over the years, as it wasn’t required for reporting or customer servicing at the time? Legacy systems will generate a plethora of reports and store huge amounts of data according to specifications, but it will traditionally only be a subset of the full data. Complete, real-time data serves as the bedrock upon which AI algorithms operate, making it essential for the industry to harness the very latest in event streaming technology to ensure that the complete record of data is available in real-time now and available in its entirety to meet future needs.

The potential of AI in Financial Services

As AI relies on data to learn, analyse patterns, and make informed decisions, it must have all of the data at its disposal. In financial services, where precision and timeliness are paramount, the need for accurate and reliable real-time data is heightened. Whether it’s for risk assessment, fraud detection, customer service, or investment strategies, AI systems heavily depend on the quality of the data they are trained on. Poor-quality, fragmented, or outdated data can lead to biased models, erroneous predictions, and compromised decision-making, ultimately undermining the very purpose of implementing AI in financial operations.

Risk Mitigation and Compliance

One of the primary functions of AI in financial services is risk mitigation, and it has been used as such in more basic forms for some time. However, the more we try to do with AI, the more important it is to have access to comprehensive and high-quality data. Financial institutions must navigate a complex web of regulations, and AI can assist in ensuring compliance. However, relying on flawed, partial or outdated data can expose organisations to regulatory scrutiny and unnecessary risk. High-quality data, on the other hand, facilitates precise risk modelling, enhances regulatory compliance, and fortifies the overall integrity of financial operations in the face of ever-changing market conditions.

Fraud Detection and Prevention

In the realm of financial services, fraud poses a persistent threat. AI plays a crucial role in identifying and preventing fraudulent activities, but its effectiveness is contingent on the quality of the data it analyses. High-quality real-time data allows AI models to discern legitimate transactions from potentially fraudulent ones with greater accuracy and speed. Timely detection of anomalies in transaction patterns, user behaviour, and account activity is only possible with a robust foundation of accurate and timely data.

Hyper-personalisation

Customer expectations from their banks are changing rapidly, and the needs of different generations are often completely different, so the role of AI is becoming increasingly pivotal. AI enables financial institutions to offer personalised services and recommendations by analysing customer behaviour and preferences. It can also help banks learn more about their customers to help them in challenging times. For example, if a customer’s end-of-month current account balance is getting lower each month, the bank can potentially reach out to see if there has been a change of circumstances that might be eased through the offering of new products or services or possibly some form of forbearance on their lending products – before they start to default. Banks do not want bad debt, so they are keen to find solutions that meet the needs of the customer and the bank. But catching it earlier, with a little help from AI – makes it easier for solutions to be offered.

To achieve this level of hyper-personalisation, AI systems must have access to comprehensive and accurate customer data. High-quality real-time data empowers AI to identify meaningful patterns quickly, enabling personalised product recommendations, targeted marketing, and an overall enhanced real-time customer experience. Conversely, relying on incomplete or inaccurate data can result in misguided insights, leading to poor customer experiences.

Are you getting the data you need to harness the possibilities of AI?

Clearly, the success of AI is intricately tied to the quality of the data it processes. Financial institutions must recognise the imperative of investing in technology that can deliver high-quality, complete data to drive AI innovation successfully. Whether it’s risk mitigation, fraud detection, or customer insights, the reliance on accurate and reliable real-time data is non-negotiable.

As the financial industry continues to embrace AI, those who understand that data quality is essential to leveraging the full potential of artificial intelligence will emerge as leaders and revolutionise the way financial services are delivered and experienced. Can you access the data you need to drive your AI aspirations?