Machine Learning in Banking (FinTech)- Use Cases and Benefits

Machine learning (ML) has been making strides in consumer-facing businesses. In fact, the banking and finance sector has been leveraging it too. It is offering better experiences to their customers as well as achieve operational efficiencies with the technology. ML in banking has been helping financial institutions to better understand customer needs, identify opportunities, and thus, remain competitive. Additionally, they use it to find solutions to business problems, and streamline back-office operations.   

The use of machine learning in banking is vast globally. From public relations to operational and investment decisions, data collected and extracted using AI and ML, emreging technologies have been helping finance, banking, and business analytics areas to achieve higher efficiency and thus, reach greater heights.   

In fact, the global ML in banking market size was valued at $1.33 billion in 2021, and is predicted to reach $21.27 billion by 2031, growing at a CAGR of 32.2% from 2022 to 2031. 

Let’s now explore some use cases of ML for FinTech and the banking industry and the benefits accruing from them.   

What is Machine Learning in Banking (FinTech)?  

Machine learning in banking is used to extract customer and market insights. This is done leveraging multiple data sets collected by banks and financial institutions. This allows them to reach at optimal results, predict future market trends and also stay ahead of the competition.   

In fact, the Data Analytics in Banking Market Research report estimates that the global data analytics in the banking market will increase from $4.93 billion in 2021 to $28.11 billion by 2031, growing at a CAGR of 19.4% between the forecast period.  

As per a McKinsey report, global leading banks take decisions that are completely based on data; with fully 40% or higher of the employee base dedicated to analytics and digital, frequently by reskilling existing employees.  

How Machine learning is used in Banking  

ML models organize and analyze the banking and finance data. This includes past transactions, customer service chats with bank representatives, corporate documents, etc., using multiple ML algorithms. This is done to reach a deeper customer understanding. This facilitates accurate and informed decisions, which is highly important for institutions dealing with hefty amounts of money every day.   

The growing interest of banking and finance institutions in ML is due to the patterns identification capabilities of algorithms. These patterns are analyzed for detecting correlations among zillion sequences and events. This helps to uncover valuable insights and information that have been buried under piles of data and missed by the human eye.   

Predictive analysis facilitated by ML enables FinTech to take strategic decisions in line with the current market dynamics as well as new potential opportunities.   

A Syntellis ‘2022 CFO Outlook for Financial Institutions’ report predicts that by 2024, financial leaders anticipate adopting predictive analytics (42%), artificial intelligence (30%), and machine learning (26%) more frequently.   

Let’s now look at some of the use cases of machine learning in banking and finance industry.   

Machine Learning in Banking: Use Cases  

Banking and financial institutions are gradually realizing the importance of machine learning with rapid adoption and hence, AI and ML in finance will become the leading investment in the next 5 years, along with institutional tools for unification and management of existing workflows.    

The globally established and known financial services brand, PayPal invests in machine learning, deep learning in particular, for financial monitoring and fraud detection.   

This means that the technology of ML is as robust in practical application as it is in theory for the banking and finance sector. It empowers the banking and finance industry to better evaluate and manage investments, opportunities, and resources.   

Following are the use cases of machine learning in banking and finance:   

1. Customer Onboarding and Document Processing  

Customer onboarding is the first impression that the customer has about the institution’s working. Thus, the onboarding process should be smooth with an interface that is easy to understand and navigate.   

AI and ML gather and study the behavior and patterns of millions of users on the web. It learns about user journeys and interactions and studies the effect of small changes in user decisions on conversion rates.   

Based on this analysis customer behavior, changes, and improvements are created in the interface of the customer onboarding page and overall processes. Additionally, using such technologies, multiple processes like onboarding can happen from anywhere, in minutes with just a few clicks.   

Furthermore, document processing, which is traditionally a crucial and labor-intensive task, has been made easy and efficient using machine learning. It decreases the time taken to organize, label, classify, and process financial documents. Moreover, when the optical character recognition (OCR) feature is applied, ML models can manage and process paper documents as well.   

Traditional banks that still rely on paper forms during the onboarding process, hence, too stand to benefit from ML-based document processing. Scanning an ID or an invoice is made easy using machine learning.   

2. Fraud Detection and Prevention   

The banking and finance industry is a common place for fraudsters and hackers. Due to its monetary nature and huge scale of operations, the sector is bound to have some loose ends, loopholes, and gaps, and attracts many notorious minds.   

Machine learning here plays a pivotal role. Banks and financial institutions use ML for fraud detection. AI and ML can evaluate huge amounts of data and data sets of simultaneous transactions in real-time, to identify any anomalies. It minimizes human intervention and updates the models to keep pace with the time and new fraud tactics.     

Using ML, banks and financial institutions label past data as fraudulent or not fraudulent. It allows them to keep training the ML models to detect potential future anomalies, by inserting large volumes of historical data and initiating analysis of customer behavior and patterns. ML models can additionally assist banks with quickly identifying user activity, verifying it, and managing cyber-attacks quickly and effectively.  

Danske Bank implemented an ML-driven fraud detection system. It was picking up 1,200 false positives per day in its transaction monitoring and 99.5% were false positives in fact, before ML intervened.    

With machine learning they were able to reduce false positives by 35% and improve the detection of true positives and actual fraud at the same percentage. Additionally, with deep learning, the numbers almost doubled to a 60 % reduction in false positives and an almost 50% improvement in detecting actual fraud.   

3. Portfolio Management  

Portfolio management is a crucial aspect of the banking and finance industry. It uses statistical points of the issue and automated algorithms to digitally manage wealth as well as optimize the client’s asset performance.  

Clients add their financial goals or other details and aims, and the robot advisor then suggests and assigns some current assets to investment variants and opportunities.   

BlackRock Investment Company, for example, offers Aladdin, an OS that provides investment managers in the FinTech sector, with risk analytics and portfolio management software tools, using ML.   

4. Process Automation  

From bank audits, and document processing, to employee training and management, all these mundane yet important tasks are automated using machine learning. ML techniques facilitate the review of thousands of pages, and identification of important documents, details, and information. This saves banks billable hours and resource requirements and enhances the efficiency of services.   

Some examples of process automation in banking and finance using ML include chatbots, paperwork automation, and employee training gamification.   

Quontic, a US-based bank, adopted an ML-powered task automation product to work in tandem with its legacy platform. With this solution, the bank recorded a 210% increase in the total number of banking customers along with a 261% increase in retail deposits, among other things.   

5. Regulatory Compliance  

Regulatory compliance is an indispensable aspect of the fintech industry. Failure to comply can cost an institution a lot of time and money. Additionally, an investment of even billions does not necessarily mean effective compliance. The machine helps with this.   

RegTech or regulatory technology applications are used to ensure regulatory compliance for banks and financial institutions. ML algorithms scan and learn from regulatory documents which helps them to conveniently recognize correlations between guidelines.   

They help the fintech industry to comply with ever-changing regulations in the financial landscape by constantly tracking and monitoring regulatory changes in real-time. ML enables these organizations to detect anomalies and make sure that every transaction complies with regulatory requirements.  

6. Credit Scoring  

Credit scoring is done to evaluate the credit history and worthiness of a customer. It informs about the likelihood of repayment of credit or debt by the customer.   

ML scoring decisions are taken factoring in a lot of elements and as a result a lot of data such as work experience, earnings, past transactions, and analysis of the same and credit history. A mathematical model based on accounting and statistical methodologies, the solution can provide more accurate and reliable credit score assessments than traditional systems.   

These systems do their work without biases, discriminatory opinions, and room for human errors. It has enabled banks and financial institutions to cater to a wider audience and credit seekers in a more fair and equitable manner.  

Equifax, for example, uses ML along with other FinTech solutions in credit scoring to offer credit ratings and demographics to businesses. Additionally, it offers commercial credit monitoring and fraud prevention services directly to consumers.  

7. Hyper Personalization and Customer Retention  

Analytics from machine learning help banks and financial institutions to personalize customer interactions and experiences through offers and services that are tailored to their needs. Using ML, the banking and finance industry monitors and predicts customer churn by monitoring behavioral changes. Personalized services also allow institutions to forge and nurture customer relationships. 

Chatbots are also used to personalize the customer experience and as a powerful customer service tool. Bank of America’s ML-powered chatbot Erica, launched in 2018, has handled around 1 bn customer interactions so far.   

Further, after 4 years of continuous training, testing, and self-improvement, the bot was capable of solving over 1mn unique customer queries in 2022. And more importantly, she can offer personalized financial advice to the bank’s customers.   

Customer acquisition is harder than retention. Hence, banks use ML-enabled analytics to identify customers who could discontinue opting for the institution’s services soon and act quickly to retain them. Machine learning helps to personalize the customer journey and retain customers.   

TransUnion bank, for example, partnered with ML-powered budgeting app Mint, to offer tips on credit score enhancement to its customers, driving loyalty and increase in customer lifetime value.   

Must Read: AI in Finance – Applications, Benefits for Fintech Industry

Benefits of Machine Learning in Banking  

As noted above, there are multiple machine learning use cases in the banking and finance sector. But why?   

The primary reason why machine learning is so successful in the banking and finance sector is the data-rich nature of this industry. Machine learning uses as well as thrives on data, the greater the amount of data, the better the results.   

The accounting record and other numbers that the finance industry has been saving up for decades would finally be leveraged to derive market and customer insights and drive business growth.  

Let’s look at some of the benefits of machine learning for FinTech.   

1. Automation of Mundane Tasks/Processes  

Customer service is where banking and finance leverage automation through machine learning. Machine learning algorithms interpret behaviors and identify patterns, facilitating classification, narrowing down, and solutions to customer problems and issues without human intervention in the customer services department.   

This is used to enhance customer experience.   

2. Personalized Customer Services   

While machine learning helps in automating mundane yet crucial customer-facing tasks, it further helps to make them more personalized for the banking industry.   

Large data gathered by AI and analyzed by ML allows banks to offer more tailored offers, services, and marketing campaigns to customers. Studying customer behavior, patterns, and tendencies allow banks and financial institutions to offer more relevant services to their valued customers, analyzing all the potential risks and solvency aspect.   

3. Loan Approvals with Lower Risks  

One of the most crucial applications of machine learning in banking is credit scoring. ML-based systems can leverage historical data to analyze the behavior, and patterns of an individual or company to help a lender or any financial institution decide on their creditworthiness.   

Machine learning algorithms compare aggregated data points with those of thousands of other customers to generate an accurate risk score. If a risk score is under the threshold set by the lender, a loan will be approved automatically. 

By going deeper into the complex data layers and carrying out comprehensive calculations, machine learning-powered systems help lenders and banks to expand and improve their market share, while approving secure loans.   

4. Lower Operational Costs and Risks  

By embedding ML and AI systems, banks and financial institutions automate back-office operations. This helps them to bring down the number of human errors, speed up documentation processing workflows, reduce operational costs as well as enhance the quality and accuracy of the tasks.   

For example, robotic process automation (RPA) software when used mimics digital tasks performed manually. This brings more efficiency and accuracy in error-prone processes. Using NLP and multiple other ML technologies, the banking and finance industry automates multiple workflows efficiently.   

5. Effective Decision-Making and Investment Evaluation  

Machine learning can process massive amounts of data, internal as well as data from other sources, in real time. This can be done to understand biases, risk tolerance, investment tendencies and preferences, and time horizons. Learning about these factors helps banks and financial institutions to better evaluate investment opportunities and take more informed decisions, with fewer risks involved.   

6. Reinforced Security, Fraud Detection, and Compliance  

ML enables banks and financial institutions to efficiently monitor and track transactions, and customer behavior. This helps them to find anomalies in real time, reduce the chances of false positives, and ultimately, prevent fraud.  

Furthermore, ML-powered virtual assistants help the banking sector to log information to additional compliance and regulatory systems, bringing down the overall risk.  

Conclusion  

Machine learning in the banking and finance sector is a powerful tool that has the potential to manage investments, risks, and resources. This industry runs on enormous amounts of data and involves high financial stakes, must leverage machine learning, and data analytics more than any other industry.   

The use cases of machine learning in banking and finance are enough to highlight how many areas of this industry could use this technology to their benefit.   

Knowing about ML is not enough though. How one implements it in their organization with minimal effort and for maximum results is an important part. And this is exactly where BigOh enters the picture.  

We offer specialized AI/ML services that include ML development, real-time analytics, NLP consultation and implementation, deep learning, AI app/web and smart chatbot development, etc.  

BigOh’s dedicated development team builds the most reliable, intelligent, and customized ML solutions to cater to the unique needs of businesses across multiple industries.  

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