The rise of challenger banks has been a particular hallmark of the fintech industry over the last decade. Created to disrupt the traditional banking sector, challengers are full to the brim with innovative, often digital offerings aiming to serve customers in a variety of ways. With the customer taking centre stage and newfound co-operation with incumbents, this month we explore some of the classic attributes of challenger banks and their efforts to stay one step ahead of the industry.
Artificial intelligence (AI) and machine learning (ML) are both becoming increasingly commonplace within modern banking infrastructures; being a particularly popular mode of technology amongst challenger banks and those who seek further complexity within their own services.
Although their capabilities preceded their very mention, the future of AI and ML within the banking industry hinges upon many different variables. Indeed, AI truly has the ambition and ability to go in any direction, and as part of our April focus on challenger banks, today we’re speaking with some of the most renowned industry experts to find out more about what exactly that direction might be.
Bias and efficiencies
According to Louis Brown, head of data science and advanced analytics at Chetwood Financial, both the potential for bias and maximum efficiency must be recognised to facilitate the wider adoption of AI: “The EBA discussion paper on ML models shows that regulators are thinking about their approach to using ML models in banking, which will hopefully lead to wider adoption of AI.
“To ensure success, we technicians need to be better at ensuring that we detect bias in our data and measure it in our models – it’s something that should be part of all banks’ model review processes. We also need to embrace the efficiencies of using AI – such as onboarding customers in retail banking, and the use of AI in driving continuous improvements.
“I believe that AI has a bright future in banking – widespread adoption will lead to improved accuracy where models are already adopted, the introduction of novel solutions to ongoing challenges, and ultimately more efficiency in the banking system.”
Speed of accuracy
The way Johnny Steele, head of banking at SAS UK and Ireland sees it, the use of AI will go a long way in automating processes, allowing the industry to make quicker, more accurate decisions: “The future is operating across a connected platform, which ensures technologies such as AI and data analytics are able to learn from the full complement of customer and company data and are highly adaptive to changes, threats and all new regulatory requirements.
“Banks need insights which are both fast and accurate. When it comes to satisfying customers’ evolving needs and remaining compliant (as regulations are updated, especially with potential new regulations around climate risk) this speed and accuracy is not a differentiator, it is essential. Repetitive processes can also be automated, to enable cost savings and enable humans to focus more time on where they can add value.
“By transforming legacy processes and accessing advanced cloud-native analytics, banks will enable faster, smarter decisions which elevate the customer experience. This approach can also democratise the use of data and analytics where low code/no-code solutions can be used by employees who are not data science experts.
“The key for banks is in overcoming security and cost concerns relating to the cloud, accessing the right blend of expertise and avoiding too much dependence on a single cloud provider.”
The rise of an ethical framework
Marcus Hughes, head of strategic business development at Bottomline, largely agrees with Steele, but emphasises how the technology must remain inside the bounds of ethical regulations: “AI/ML are key tools to accelerate and streamline a wide range of activities and improve customer experience. For example, know your customer (KYC) and digital on-boarding of new customers to verify their identity online.
“Banks are also exploring ways to automate their lending, using AI and advanced algorithms to decide who to lend to. This is based on historical data which is held on different types of borrowers, who can be grouped by categories such as postcodes and employment profiles.
“However, UK regulators recently warned banks that they can only deploy AI if they can prove it will not worsen discrimination against minorities, who have historically struggled to access reasonably priced loans. This could lead to a negative cycle where those in groups who have traditionally had high defaults are charged higher interest rates, which in turn makes them more likely to default.
“The regulators are therefore discussing an ethical framework and training around AI, including some human oversight and a requirement that banks can clearly explain the decisions taken.
“An innovative way to prevent fraudulent payments and operational errors is to use ML to monitor transactions and employee behaviour. ML enables the fraud system to learn and update itself on what is normal behaviour so that it can raise alerts for abnormal and potentially fraudulent activity and transactions.
The power of partnerships
Stacey Conti, VP of global strategy, sales and partnerships at Sybal.io, recognises the importance of industry partnerships in the future of AI-driven banking: “It is hard to predict the future and the impact AI will have just on the banking industry. I believe AI is here to stay and slowly smaller institutions are adapting by partnering with fintech companies to bring the same tools to community banking as the big players with more money to spend.
“Innovation is what keeps the financial system moving. The US is finally embracing much of the technology already being used in the European and Latham systems.”
Increased personalisation
Kavita Singh, VP of AI product management for Payrailz, underlines how AI will become one of the major drivers for increasingly personalised banking services: “The future of AI in banking is bright. We live in an increasingly data-driven world. The more data we can feed to AI and machine learning, the more helpful it can be.
“As consumers learn more and more on the digital channel, financial institutions must find a way to personalise each accountholder’s service, without the personal connection of the branch.
“AI and machine learning make it possible to use consumers’ data to help digital banking feel more personalised. Being able to offer financial products and insights tailored to each specific person and their unique needs is something we should see more of in the future of banking.”
Data quality
The most effective technology sits upon the sturdiest foundations, and as Peter Sanchez, global head of banking and treasury services at Northern Trust puts forward, it’ll be the quality of the data that ultimately decides the future of AI: “In our speciality of institutional banking and treasury services, we see great potential for AI and machine learning to support greater transparency, efficiencies and faster timeframes for regulatory analysis activities.
“Northern Trust is a member of the Regulatory Genome Project, launched by the University of Cambridge in conjunction with Regulatory Genome Development Limited, which seeks to develop an open-standard framework for classifying regulatory information using AI.
“Good quality data is the foundation on which future AI capabilities are built, so ensuring and solving for data quality will be essential for the future development of AI in any of its applications.”
