Many banking decision-makers still consider machine learning technology, a subset of artificial intelligence, irrelevant. And too pricey anyway.
But there’s no denying that the integration of AI has many benefits, especially given the highly data-driven nature of today’s market. Ready for some big numbers? A recent study by Juniper predicts that spending on machine learning (ML) in fintech may grow tenfold by 2022. And, thanks to new developments in analytics and accessible computing power, fintech platform revenues for unsecured consumer loans issued using ML technology will jump by 960% to $17 billion globally by 2021. As for the pricey argument, machine learning is getting more and more budget-friendly with many new industry players offering solutions for processing huge datasets.
Machine learning may sound like something from a distant future but it has actually been part of our everyday lives for a while. ML technologies, which apply algorithms to analyze datasets, spot patterns and create customer insights, are already used by Google Maps, Netflix or Amazon product recommendations, to name but a few. And ML has plenty of possible applications in banking. Ajay Vij, Vice President responsible for European financial services at Infosys, lists repetitive work (e.g. back-office functions), high-accuracy tasks (e.g. loan underwriting) and even informed decision-making (like providing financial advice), for example.
5 ways banks can put self-learning algorithms to work
Risk assessment: ML tools can be a tremendous help for lenders when it comes to evaluating risk scores and making credit worthiness decisions more accurate when extending loans. They basically speed up the entire approval process. Take Norwegian digital bank Instabank, for example. They have teamed up with risk analytics provider Provenir to launch a cloud-based platform so they can make nearly immediate decisions on customer loan eligibility. The system mostly uses traditional data but it’s possible to even incorporate social media data into the assessment methods.
Customer service: Virtual banking assistants or chatbots use machine learning to better understand customers’ behavior, track their spending and saving habits, and assist them in everyday transactions. What’s more, they can even give them tips on how to manage their finances better. Bank of America’s virtual assistant, Erica, is a prime example. Introduced in 2016 as a new feature of the mobile banking app, it makes use of predictive analytics and cognitive messaging to “provide proactive guidance” to customers and “anticipate their financial needs”.
Equity trading: Brokerages already use ML methods to make predictions for equity trading by making the assessment of factors like market volatility completely automated. But ML tools are handy for other things too: JPMorgan, for instance, launched a predictive recommendation engine in 2016 to identify clients who should issue or sell equity. This application was later expanded to debt capital markets, similarly basing predictions on client financial data, issuance history and market activity.
Detecting fraud: Machine learning programs can flag up anomalous actions for near real-time fraud detection. They can spot patterns based on the past behavior of account owners and generate alerts in case of uncharacteristic transactions. According to Infosys, such predictive analytics can also be used as an anti-money laundering tool to trace the true source of money by identifying disguised illegal cash flow.
Regulatory compliance: Fintech companies are offering ML solutions in many new areas. Hexanika, operating in the US and India, has developed a software application that uses ML processes to cut the burden of banks in statutory reporting on accumulated data. The tool automates data management and streamlines the compliance process, which are traditionally labor-intensive manual tasks. It also keeps up with all regulatory updates and can be easily connected to existing systems without architectural changes.
Save costs and increase productivity with machine learning
Ajay Vij at Infosys writes that using ML tools can save training costs and enhance customer experience by offering personalized service at contact centers. It can also provide more opportunities for cross or up-sell through targeted communication on new products. Machine learning automation can increase productivity too by cutting time to complete routine tasks and ensuring higher accuracy. To top it all, ML can be programmed to follow protocols to simplify meeting regulatory standards.
But it’s not all blue sky. Here are a few pointers on what to take into account before deploying machine learning at your organization. First, it’s crucial to have a plan at hand for dealing with employees in manual jobs who are soon to be replaced by machine learning. You also need to pick the right strategy to avoid using ML for just routine applications with repetitive tasks. Also, remember to get all the protocols right when handling large datasets containing sensitive and protected customer data to prevent violations.
Lack of experience is a barrier
There are barriers stopping banks and other financial services firms from leveraging the full potential of machine learning. Let’s take a look at insurance companies, for example. Most of them are well aware of the possible advantages of machine learning in automation, productivity and cost saving. But only 12% do actually use these opportunities, according to a survey by analytics platform developer Earnix. The biggest problem is undoubtedly the knowledge gap: 82% of respondents described themselves as beginners in machine learning. Another obstacle is the lack of experienced hires available. Analytic talent is hard to come by, which is not at all surprising considering that only a handful of universities teach courses in machine learning yet.
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