As AI continues to present new opportunities, the finance industry is putting its potential to good use. Predicting future trends, however, comes with its challenges. There are clear benefits of using AI in finance, but there are risks associated with implementing new technology.

AI improves financial inclusion by ensuring banks can determine credit scores, which is a critical factor in money management. AI can draw on social media or other sources to understand the ability of people to repay a loan. Reducing the constraints with financing means institutions can focus their efforts on better access to finance and growing the economy. ML and AI models in finance utilise big data to generate accurate predictions about the market. They assess multiple risk factors and determine the investment performance against various industry and economic scenarios. This process reduces the overall investment risks for finance businesses and their customers.

AI also supports investors in generating insights from multiple areas to develop their investment strategies within a relatively short timeframe. Several research groups are discovering that AI-based investments are exceeding the performance of conventional ones. AI and ML can improve efficiency and inclusion, but they also have two main risks.
AI-based credit scoring models may cause unfair lending processes. While a credit officer will be cautious not to include gender or race-related factors in scoring, ML may mistakenly consider these factors. ML models are only as reliable and accurate as the data they are made with. If models consist of poor data or data that reflects core human prejudices, it may generate inaccurate results, even if the data generation improves. The second challenge is that algorithms can also make finance businesses vulnerable to cyberattacks. It’s easier for cybercriminals to take advantage of models that all activities in the same way, compared to human systems, which work independently.

Policymakers need to accelerate their resources to combat the risks related to AI and other technology. One important method is improving the overall communication process. For example, finance-related businesses should instruct all users if a particular service uses AI. They should also explicitly identify the limitations of AI models so customers can make their own informed financial decisions. This process creates further trust and confidence and promotes a safer integration of new tech like AI.

Furthermore, policymakers should highlight human decision-making over AI-focused decisions. This approach is especially relevant for high-value areas like money lending, which can have a significant impact on the customer. Customers will feel more empowered in this scenario which allows them to adapt to the outcome of AI models. Users should have the option to opt out of having their data measured within AI models. Over extended periods, these measures increase the level of trust in new technology, like AI and ML.

Policymakers need to ensure that finance-related businesses test AI and ML models before implementing anything to remove possible bias. Testing allows businesses to check that the models are operating as expected and are meeting current rules and regulations. AI and ML can help finance businesses create a more accurate forecast of financial markets, but it can’t be considered more than a forecast. New technology like AI and ML should be viewed as tools with considerable potential if all the associated challenges are dealt with correctly.