When we consider the most important factors for consumers of financial products, trust, transparency, and security are paramount. Decisions on where to put our hard-earned money and who to let take care of it are not made lightly. But now that the days of the friendly high-street branch where customers would visit to make weekly deposits are over, it is the digital experience that holds the key to the success of financial institutions.
Artificial intelligence has become an essential part of the banking, financial services, and insurance industry in recent years, transforming how products and services are offered. Its ability to assign credit scores, assess insurance claims, and optimise investments is transforming the sector, but are consumers ready to trust AI with their life savings?
In a 2023 study, Salesforce asked over 6,000 customers worldwide about their sentiments on AI and the digital experience. About 23% said they did not trust AI, and 53% said they would switch providers if a better digital experience were offered. Customers in the survey also indicated that knowing their data is being used effectively and safely is paramount.
The quick wins of using AI in financial products
According to McKinsey, AI technologies could potentially deliver up to $1 trillion of additional value each year for the global banking industry. So, it’s a crucial part of any financial product strategy and here is why:
- Personalisation is everything in the digital era: Machines can predict customer behaviour and understand motivations and preferences faster than ever, which is a huge relationship-building asset. Collecting data and using it to tailor online experiences is key to customer satisfaction across multiple sectors, and financial products are no different.
- Productivity, increased revenue, and lower costs: Most Gen Z consumers and digital natives will never visit a physical branch because these days, accounts can be set up, serviced, and maintained completely online. The online experience is so important that, when executed effectively, it can yield bigger returns and save time and money. Through automation, financial institutions can reduce human error and maximise their resources elsewhere.
- Reducing risk: AI also helps to tackle financial risk because organisations now have more data available to them in a much quicker timeframe. It’s great at spotting fraud and can monitor huge numbers of transactions at a time.
Potential pitfalls
As with every new digital innovation, there are also risks attached. Cyber-attacks are a threat; if people do not feel their data is safe, they will take their business elsewhere. Entrenching bias is also a big consideration – if the AI is trained on decisions previously made by humans, bias will exist, so the question is, how do we manage this element?
Black box problem
Complex AI algorithms can function as ‘black boxes, ‘ creating a lack of trust and uncertainty. A black box is a solution that makes decisions about vast amounts of data, where a user can understand the input and output but has no idea what the process was to reach a decision.
While the outcomes of black box algorithms might be successful and speed up decision-making, the lack of transparency is a cause for concern and can lead to unfair outcomes for certain demographics. For example, the relationship can turn sour if a customer is turned down for a loan or a bank account without evidence to suggest why.
Transparent and explainable AI
Customers need to understand why their loan was denied or if an investment recommendation was made. There is a demand for AI to be fair, transparent, and accountable, but how can we lift the lid on ‘black box’ solutions to ensure they are ethical and accurate? This is where we come full circle and either bring a human (preferably an expert) to help or invest in other supporting technology.
According to a PwC study, a number of methods for generating AI explanations exist, ranging from classic black box analysis approaches that have been used in science and engineering for generations to the latest methods designed for Deep Neural Networks (DNNs).
In conclusion, integrating AI into financial products offers immense benefits but also comes with significant challenges related to transparency and accountability. Financial institutions can foster trust, enhance customer satisfaction, and drive sustainable growth in the digital era by prioritising explainability and investing in transparent AI solutions.
