How fintech startups are utilising ai to transform the banking industry
The UK banking industry has undergone a significant shift thanks to fintech startups like Starling Bank, Monzo, and Atom Bank. What was once seen as a stagnant industry has been transformed into one that is rapidly advancing.
With most of these vanguard banks based in the UK, it brings to mind ‘The Old Lady of Threadneedle Street’, also known as the Bank of England, founded in 1694. Peering back in time, if history has shown us anything, it's that progress is inexorable. We are on the frontier of a future that will transform the way we interact with, store and spend money.
As a result, new regulations have been introduced, placing the consumer at the forefront. The Competition and Markets Authority and the Financial Conduct Authority introduced a financial comparison rule requiring banks to share data and allow consumers to compare them against each other. This increased transparency will create a more inviting environment for fintech startups in the UK, which will ultimately result in better opportunities for new players.
AI is not a new technology, but its application within financial institutions has resulted in solutions that were once unimaginable. It is now being used to save time and money by efficiently analysing vast amounts of consumer data, pinpointing valuable uses in real-time, and recommending actionable plans.
Here are four ways fintech startups are using artificial intelligence (AI) to reimagine the banking industry:
Analyse customer's financial data and provide personalised recommendations to better save, invest, and manage their money. Traditionally, this process was done manually by evaluating the customer’s credit history, income, amongst other financial data. Now, AI algorithms can be trained to sift through the historical data of loans and creditworthiness of each customer. The algorithm can then use this information to predict whether a loan application is successful or not. The amount of data the algorithm will have access to can cover an applicant's credit score, income, debt-to-income ratio, employment history, and other financial data - helping to make better informed decisions.
Identify unusual patterns or activity in a customer's account to detect and prevent fraud far more efficiently than previous tools allowed. Traditionally, banks relied on the tedious process of manually reviewing customer transactions to pin-point fraudulent activity, but with the increase in transaction data and the complexity of online fraud, manual processing is no longer effective. Now, AI algorithms are trained to recognise fraudulent transactions by locating patterns. This data can then be used to identify and flag potential fraud.
Automate routine tasks that customers carry out, such as opening a bank account, KYC (know your customer) processes, and loan applications, freeing up staff to focus on other activities. This task can be done through the use of chatbots and virtual assistants. A chatbot can be programmed to ask a customer for necessary information, verify that information using AI, check their credit rating and then determine whether the customer is eligible for an account or loan.
Sort through and analyse large datasets to help banks better understand and manage risk. Machine learning algorithms can be used to comb through data and pinpoint patterns and anomalies that could indicate risk. By pattern matching, flagged transactions can be investigated by AI to better reduce financial fraud.
By utilising the power of AI, fintech startups are automating tedious and time-consuming tasks, allowing them to focus on developing new products and services, resulting in a more efficient, personalised, and secure banking experience for customers. As our world changes, technology will continue to adapt to meet our needs.
