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Machine Learning in Banking: Use Cases, Benefits, and Future Outlook

Having reached $3.88 billion in 2020, the global market for artificial intelligence banking technology has since been growing at an astonishing CAGR of 32.6%.

While some financial organizations remain adamant about adopting AI, 85% of commercial banks already use the technology at least in one business function.

Despite the recent advances in computer vision (CV) and natural language processing (NLP), it is machine learning that continues to dominate the FinTech AI market.

A subset of artificial intelligence, ML involves training algorithms on labeled or unlabeled data, detecting patterns, and assisting financial services specialists in performing mundane tasks or making well-informed decisions.

In this article, a team of innovation analysts from Symfa will zoom in on the most potent machine learning (ML) applications in the banking sector. Additionally, we’ll look into the benefits the technology brings to the table, as well as its future trends.

Machine Learning in Banking: Top 5 Use Cases

Credit underwriting

Assessing the creditworthiness of individuals or business entities has always been effort and knowledge-intensive. To minimize risks, a credit specialist had to review an applicant’s financial information, including credit reports and employment history, evaluate the person’s ability to pay the loan considering the current economic situation, and calculate credit scores. When performed manually, the process takes anything between several days and several weeks. And it’s prone to errors, too. The strain the COVID-19 pandemic put on the US banking industry highlighted its credit underwriting inefficiencies, which potentially lead to banks giving low credit scores to solvent customers. Unlike humans, ML models are mainly free of bias and can process significantly more data within a shorter timeframe. Additionally, machine learning takes a more granular approach to credit underwriting thanks to its ability to analyze both financial data, such as income and credit scores, and alternative information (e.g., utility payment and rental history).

Risk management

The banking sector is exposed to various risks, ranging in severity and complexity from human errors to high-profile cyberattacks and compliance violations. To mitigate these challenges, banks engage in robust risk management practices, which include identifying potential risks, assessing their likelihood and impact, and developing strategies to mitigate them. By tapping into ML-assisted predictive and preventive analytics, banks can take their risk management capabilities to the next level. For example, machine learning models can eliminate situations where loans are granted to borrowers with a poor credit history. Similarly, algorithms monitor historical and real-time data to assess how market trends and fluctuations could affect a bank’s portfolio. Advanced ML systems can also automate compliance checks to validate that banks adhere to the evolving financial market regulations. One of the early examples of ML-driven risk management comes from Barclays, one of the largest and oldest banks in the UK. The company uses machine learning in a number of applications, achieving excellent results in transactional data analysis and fraud prevention.

Fraud Detection

Fraud remains a daunting challenge across all industries, consuming up to 5% of corporate revenues globally. To avoid financial and reputational damage, the banking sector utilizes numerous instruments to flag and prevent fraudulent activities. Such instruments and methods encompass transaction monitoring, anomaly detection, identity verification, user location tracking, and behavioral analytics. In the age of data deluge, however, traditional fraud detection tools have started to hit their limits. ML’s promise in this field stems from the algorithm’s ability to crunch large volumes of financial data down to meaningful insights and analyze it against more parameters. The technology helped Danske Bank overhaul its rule-based fraud detection system, reducing the number of false positives by 60% and improving the overall efficiency of the fraud detection process by 50%.

Workflow Automation

Banking, which is arguably one of the most digitalized sectors, is currently undergoing a second wave of automation. By the end of this phase, artificial intelligence solutions will perform up to 25% of banking tasks across all functions, saving employees time for higher-value activities. In comparison with rule-based business process automation (BPA) tools and robotic process automation (RPA) solutions, ML-infused intelligent process automation (IPA) systems’ magic stretches far beyond copying and pasting data on the interface level. For example, they can process unstructured data, such as paper-based documents, emails, and images. Such tools can also monitor market trends, assist managers in performing customer background checks, and take over customer support operations. These improvements can lead to major cost savings for the banking industry. AI-based chatbots alone could save the sector $7.3 billion!

Customer Service Personalization

While 78% of customers expect their banks to deliver highly personalized service, only 44% of banks are actually able to provide it. Once again, the culprit is legacy systems’ inability to collect, process, and interpret an abundance of customer data, which includes transaction history, past interactions, demographics, and more. By tapping into ML-based financial software development, banks can up their personalization game. For instance, algorithms can help segment clients into narrower groups based on age, income, financial goals, and transaction patterns. Or tap into predictive analytics to anticipate churn rates and reengage customers with highly relevant product and service suggestions.

While these use cases are just the tip of the ML revolution in banking, it’s safe to say the technology is poised to transform the banking industry as we know it.

Let us summarize the benefits machine learning offers:

  • Using machine learning, banks can gain deeper insight into customer behavior and needs. This, in turn, empowers financial organizations to develop and customize products and service offerings.
  • ML-driven IT systems can automate repetitive and labor-intensive tasks in the banking industry, which leads to significant cost savings. Additional cost reductions can be achieved with machine learning tools for fraud detection and compliance assurance.
  • Machine learning solutions can absorb and interpret miscellaneous data in unlimited quantities, supercharging banks’ analytics capabilities and driving better-informed decision-making.
  • ML systems allow banks to augment their in-house data with information from external resources and applications and gain a 360-degree view of their operations while considering current market trends.
  • ML solutions pave the way for hyper-personalization, helping banks adapt their offering to the needs of digital-first customers.

Machine Learning in Banking: What’s Next?

Artificial intelligence has already saved banks over $447 billion, and this alone signals the technology’s bright future in the financial sector.

As generative AI solutions are taking the financial service by storm, we’re likely to see more banks customizing large language models (LLMs) to meet their evolving automation, customer service, and analytics needs.

However, this doesn’t mean that traditional ML solutions are becoming a passing trend.

In 2024 and beyond, we can expect the convergence of different AI techniques and subsets, from classic machine learning to computer vision and generative AI systems.

In this synergy, generative AI could produce synthetic data for ML model training and refinement in cases when real-world data is scarce or hard to obtain due to privacy regulations.

Other potential applications involve utilizing machine learning to gather, categorize, and enhance data. This data can then be fed into generative AI systems, which will produce reports and recommendations for bank employees.

Either way, machine learning is not going anywhere, and it’s in your best interest to incorporate it into your technology stack.