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 new found 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.
Both challenger and incumbent banks are increasingly using artificial intelligence (AI) to improve both their customer experiences and their infrastructure. With the right model, one that can be changed should it make any misinformed biases, AI can massively help banks be more financially inclusive and create a better community for all. To learn more about how AI can be used in banking to increase financial inclusion, we spoke to some of the key industry players to hear their thoughts.
Nick Chandi, CEO of ForwardAI explained, “One of the ways AI directly addresses financial inclusion is through credit underwriting. Credit bureau scores have been used as the predominant signal for underwriting, but it prevents those without credit history from accessing loans. With AI, lenders can now leverage alternative data such as cash flow for credit decisioning and approve more SMB borrowers that might not have assets or long credit histories, all while increasing loan volume and thus revenue for lenders.”
Machines aren’t immune to being bias
David Royle, chief operating officer and MD financial services consulting, SRM Europe, said, “In theory, AI can help understand credit profiles, from both at risk and contextual perspective, to provide appropriately priced products and services to a broader set of socio-economic customer sets – especially those whose individual circumstances don’t readily fit traditional models of assessment.
“However, a machine can be biased too. Selection, exclusion, and racial biases are some of the many self-fulfilling fallacies a machine can inherit from its author (data guru) or data that can hinder financial inclusivity. A machine will probably never recommend a loan to a customer with bad credit or may avoid certain demographics with financial products. These biases therefore need to be resolved – something which can be approached via various methods, including: qualitative research (survey and insight); data diversity (bringing multiple sources to the training data, better data labelling and effective sampling); and monitoring and assessing the models over time to identify inherent bias.”
Johnny Steele, head of banking, SAS UK & Ireland, provided a similar view saying. “The advent of cloud-native analytics delivers unparalleled scale and agility, enabling the insight held within banks’ data to be unlocked and acted upon in real-time. This is helping banks to achieve better governance and fairer banking for all, by enabling them to make fast, accurate decisions based on data. Ultimately this is making them more data-driven, and in theory less exposed to possible bias in decisions.
“However, bias can exist within AI too, so key to fairer decision-making and financial inclusion is having AI solutions which are fair, accountable, transparent and explainable – rather than a ‘black box’ approach which just pumps out an answer – so it’s possible to understand exactly how decisions are arrived at. Models can then be adapted or replaced to ensure decisions continue to be fair and ethical.”
Breaking down disability barriers in finance
Stacey Conti, VP global strategy, sales and partnerships: Sybal, said, “One major jump forward for inclusion would be the barriers of ADA accessibility. AI can open up banking to everyone no matter their disability. A single gesture can get their loan application started. The opportunities of inclusion for consumers with disabilities is endless.”
An opportunity to build savings
Kavita Singh, VP of AI product management, Payrailz, said, “Being able to make personalised financial recommendations in this way opens doors to those that may not have much experience with managing their own finances or those who may struggle with their financial health. AI and machine learning can look for and point out opportunities for account holders to build savings or cut down on unnecessary expenses.”
Creating flexible communication channels
Peter Sanchez, global head of banking and treasury services Northern Trust, concluded, “The use of AI/ML technology, including conversation bots to deal with basic information requests and questions, can help banks to meet the needs of client groups that may benefit from more flexible communication channels such as clients that may have specific cognitive, physical or language requirements. AI also has the ability to look beyond traditional market credit scoring mechanisms and apply personalised risk decisioning and appropriate products based on recent or even real-time data – aiding financial inclusion and removing potential barriers from traditional routes.”