How Does Machine Learning in Fintech Benefit Consumers?
Conventionally, borrowers and customers have been distrustful of the mainstream banking system, primarily due to perceived complexities or a misconception that the best services are only available to the wealthiest clients.
The global shift to the ‘facelessness’ of online banking and closures of local banking branches have done little to assuage this inherent scepticism the average Joe has of the wealth institution.
While the development of machine learning and algorithmic decision-making looks set to continue transforming the way we interact with credit lenders, banks and building societies, the ‘computer says no’ mentality is still an obstacle that many fintech continue to battle with to unlock additional markets and maximise value from their existing customers.
Today, we unpick some of the more interesting examples of machine learning use in fintech, explore real-world applications, and explain why AI could significantly improve your banking experience.
Machine Learning Applications in Finance and Banking
Fintech is a prime sector for machine learning rollout. Machine learning uses computer systems to study behaviours, adopt patterns and identify variations across banking and credit networks.
Financial businesses can issue exact instructions combined with statistical modelling to create intuitive programmes that can make fast decisions and note trends or differences from the norm.
There are numerous applications, such as:
- Anti-money laundering checks
- Fraud detection
- Credit risk assessments
- Calculating pricing
- Underwriting insurance policies
- Customer support
The Bank of England researched uptake in 2019 and found that machine learning is increasingly important to financial services providers and is already used by two-thirds of businesses surveyed.
How AI Improves Fairness in Financial Lending
The inherent qualities of machine learning allow it to process data streams quickly and accurately, a technological advantage set to deliver a value of up to $1 trillion a year¹ according to research from Mckinsey & Co.
Decisions – such as what credit limit to set for a customer, how long they have to repay, whether to approve an application or which type of savings account to offer – are automated, without potential human error or unconscious bias.
One of the biggest plus points is that every interaction with a financial business is conducted based on absolute parity. AI modelling can make lending fairer by improving objectivity² and eliminating discrimination according to factors such as race or gender
The challenge for fintech is developing models that do not rely on historical decision data, which will include past inequalities in lending decisions and could potentially feed into the understanding of AI.
The study investigated this issue and reported that while a level of influence applies, a fintech algorithm charges 40% less to minority applicants than a face-to-face application process.
Reliance on machine learning isn’t without problems. Still, the key value for many groups is that every customer is assessed on an even playing field – long gone are the days when a loan application would be at the mercy of a bank manager’s first impression.
Examples of Current Fintech Machine Learning Systems
AI and machine learning sometimes sound like future-based concepts, but they are alive and well throughout the financial markets, continually being fine-tuned and refined to improve both consumer-based services and bolster business profits.
Case studies include:
- Deserve a US credit card company that can approve accounts even for applicants with no credit history or who need help to rebuild a poor credit score.
- Wonga: a South African short-term lender who uses machine learning to deliver significantly faster loan decisions and more in-depth affordability assessments.
- ZestFinance: an American insurance underwriter who adopts an AI credit modelling process to reduce underwriting costs.
The Advantages of Fintech Machine Learning for Consumers
Alongside equal consideration, consumers have considerable benefits, with machine learning a potential solution for long-held hurdles such as lengthy assessment times.
Credit borrowing is a question of balancing consumer needs with risk management and ensuring that the information provided by an applicant is correct and complete.
Lenders have an ethical and regulatory responsibility³ to lend carefully and conduct credit checks and other calculations to verify whether the borrower has the financial means to keep pace with the repayments.
Fintech gives a lender better insight into this ability to repay and can incorporate a far greater scope of data and complex calculations than any manual evaluation model.
As machine learning systems advance, they become more able to incorporate secondary factors into the assessment, reducing the potential for loan approvals that are inappropriate for the customer’s financial circumstances. Although that may sound like you’re less likely to secure borrowing when you need it, the opposite can be true.
Bank credit scoring often relies on outdated processing depending on a fixed number of information streams. In contrast, machine learning compares more aggregate data points as a kind of health checkup, including factors such as your rent repayments that usually are ignored.
Safer approvals that have correctly analysed risk and sustainability of repayment paired with faster decisions mean better access to finance and fewer barriers to support for individuals most in need of responsible lending. However, we can’t ignore the role lenders should have in providing support to those who are ineligible for a loan.
The evolution of machine learning’s power would see seamless collaboration between different financial services based on the customer’s needs.
For example, a failed applicant would be presented with options for financial literacy education, debt relief, or lower stake credit better suited to their current needs.
We’ve discussed how machine learning can improve a customer’s chances of successfully obtaining credit above but it would be naive to assume that this solves the entire puzzle of ‘the customer experience’.
Machine learning can only supplement the data-driven aspects of a customer’s application. Attention should still be given to the many human aspects of a financial service lest we run the risk of further alienating customers.
Robust customer support, live chat and phone lines still perform better than an automated chatbot or endless redirects to ‘support pages on your website.