Machine Learning Use Cases in Banking and Finance | Intellias (2022)

Machine learning in banking is gaining popularity in the FinTech sector, from public relations to investment decisions. But how exactly can tech companies incorporate this technology in finance to drive real results? In this article, Intellias lays out machine learning use cases in finance.

The profitable alliance of machine learning and finance

Custom machine learning development is used in various aspects of our lives today. It helps us get from point A to point B, suggests what to do with pressing issues, and is getting better at holding conversations. No wonder in the world of finance we keep hearing about new machine learning use cases in banking. Applications of artificial intelligence (AI) in FinTech are predicted to be worth up to $7,305.6 million by 2022.

AI and ML are the most impactful trends in the FinTech industry

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Source: Mediant – Mediant FinTech Trends Report

Machine learning algorithms used in finance work best for pattern identification. They detect correlations among tons of sequences and events, extracting valuable information that’s camouflaged among vast data sets. Such patterns are often missed or simply can’t physically be detected by humans. The ability of ML to learn and predict enables FinTech providers to recognize new business opportunities and work out coherent strategies.

A schematic view of ML in relation to AI and big data analytics

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Source: The Financial Stability Board (FSB) – Artificial intelligence and machine learning in financial services

Five notable uses of machine learning in finance

FinTech companies that are exploring machine learning in banking and finance can expect higher interest from venture funds. Venture Scanner examined funding by AI tech categories and concluded that machine learning platforms and machine learning applications not only led the sector in Q2 2018 funding but dominate the industry in all-time funding.

But what makes banking and finance one of the most-targeted business segments for machine learning? It’s definitely the tremendous volume of data and the nearly infinite size of this segment worldwide. There are many machine learning use cases in finance, including for banking and credit offerings, payments and remittances, asset management, personal finance, and regulatory and compliance services. One of the main benefits of machine learning in banking is volumes of data — including accurate accounting records and other numbers — that have been saved by financial companies for years can now be turned into effective business drivers.

Machine learning in FinTech means more loan approvals with lower risks

Interest in peer-to-peer lending has skyrocketed both on the part of borrowers and investors. Along with P2P lenders, traditional banks are also looking for new mechanisms to improve market share without additional risk. Credit scoring is one of the most useful applications of machine learning in FinTech.

Machine learning use cases in finance give lenders better insights into a borrower’s ability to pay by working with far more data and more complex calculations than conventional models. Machine learning processes more layers of data, and isn’t limited to FICO scores and income data. Such applications of machine learning in finance open alternative data sources to lenders.

Thousands of factors, such as data from social profiles, telecommunications companies, utilities, rent payments, and even health checkup records will now count. 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.

(Video) AI and Machine Learning in Banking

Machine learning algorithms at work for loan automation

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Source: Tieto – How machine learning can improve accuracy in credit scoring

What are the benefits of machine learning in banking credit scoring?

  • More loan approvals attracts borrowers who were previously overlooked.
  • Trustworthy credit scores with fewer lending risks.

Here are several providers worth mentioning in this category:

  • ZestFinance works on machine learning-based credit models to generate more profitable underwriting.
  • Deserve uses machine learning to provide users with credit cards even if they have no credit score or need to rebuild their credit.
  • Intellias has extensive experience in FinTech solutions. They’ve assisted a US-based SaaS lending provider with developing an ML-enabled credit score calculator and microservices software architecture. It runs with the help of ML algorithms and a custom-built AWS-based fault-tolerant database to get the most data about borrowers and their businesses.

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Read more: Learn how a microservices architecture helps modern businesses stay flexible, swift, and efficient

Machine learning applications in finance can help businesses outsmart thieves and hackers

A typical fraud detection process

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Source: Maruti Techlabs – How Machine Learning Facilitates Fraud Detection

Fraud in the FinTech sector is a knotty problem for all service providers, regardless of their size and number of customers. It’s a well-discussed problem with known complications. And ML might just hold the solution.

Machine learning in FinTech can evaluate enormous data sets of simultaneous transactions in real time. Moreover, the ability to learn from results and update models minimizes human input. Using machine learning techniques, FinTech providers can label historical data as fraudulent or not fraudulent. By running ML algorithms, the system will learn to recognize activity that looks suspicious. ML models can detect unusual activity, for instance in the course of an online transaction.

(Video) How AI & Machine Learning Will Transform Banking & Finance. How Is Machine Learning Used In Banking?

In 2017, a record-high figure, 78%, of the surveyed organizations were affected by fraud.

What are the benefits of ML for security?

  • Fights fraud effectively and effortlessly.
  • Prevents the subtlest fraudulent transactions that often can’t be anticipated by manually defined rules.

Comparison of rule-based and machine learning-based fraud detection systems

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Source: AltexSoft – Fraud Detection

Here are several providers worth mentioning in this sector:

  • Feedzai is a startup that offers one of the most mature machine learning engines, which is quick at taking advanced fraud prevention measures.
  • Biocatch combines behavioral biometrics with machine learning to recognize and prevent human and non-human cybersecurity threats mainly in banking, payments, and insurance.
  • Ravelin is a London-based company that uses machine learning to prevent and stop fraud in online payments.

Machine learning in banking and finance helps companies comply with ever-changing regulations

The role of machine learning in regulatory compliance

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As if billions of dollars spent on regulatory compliance were not enough for financial firms, the majority still have to deal with more new rules and regulations. Focusing on regulatory issues in FinTech and banking requires lots of time and money. Even so, this investment can’t guarantee that all new rules are followed in a timely manner.

Companies are spending an average of $1.34 million on compliance-related technologies in 2017, up from an average of $92,000 in 2011.

Among top machine learning use cases in finance are applications under the category of Regulatory Technology (RegTech). Because ML algorithms can read and learn from a pile of regulatory documents, they can detect correlations between guidelines. Cloud platforms with incorporated machine learning algorithms used in finance can automatically track and monitor regulatory changes as they appear. Banking institutions can also monitor transaction data to identify anomalies automatically. This way, machine learning can ensure that customer transactions comply with regulatory requirements.

Contact experts at Intellias if you plan to develop or scale a digital solution based on recent developments in machine learning

(Video) Practical Machine Learning: The Use Case in Retail Banking

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What are the benefits of ML in regulatory compliance?

  • Banking organizations can more successfully conform with applicable regulations, laws, and supervisory expectations.
  • Time-consuming and often tricky tasks can be performed by machines instead of humans.
  • Regulatory work can be done faster with minimized risks of non-compliance, reducing multiple manual tasks.

Here are several providers worth mentioning in this category:

  • Pendo Systems is a FinTech company that works with unstructured data to streamline the compliance process for their clients.
  • Compliance.ai is a Silicon Valley startup that uses adaptive machine learning models in FinTech to automate research and track financial regulatory content and regulatory updates in a single platform.
  • ComplyAdvantage is a US-based startup that uses machine learning to accelerate FinTech compliance and enable online fraud prevention tools.

Providers enrich the customer experience using machine learning in customer service

There are several reasons why people choose FinTech services over traditional ones. With machine learning’s ability to delve into petabytes of data to find out exactly what matters to a particular customer, financial institutions can create personalized offers. Even better, machine learning algorithms in banking and finance can analyze customer data and return predictions about a user’s preferences. This way, companies can know what services or offerings a particular client is likely to appreciate.

AI and ML platforms in the framework of customer service infrastructure

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Source: CustomerThink – Customer Service beyond the Chatbot Hype

Another example of a rewarding machine learning use cases in banking is a chatbot. Machine learning supports a new generation of chatbots that are more intelligent, human-like, and client-oriented. As chatbots learn from each interaction, the conversations they hold become more helpful and personalized. Less need to build or expand customer service departments is another great benefit, especially for small and mid-sized financial operators.

Chatbots will be behind 85% of all the customer service interactions by the year 2020.

What are the benefits of ML for customer service infrastructure?

  • Increased revenue thanks to improved user experiences and better productivity.
  • Companies that use machine learning for advanced customer service are perceived as something more in touch.
  • Clients appreciate innovation-led FinTech businesses that simplify their lives and add real value.

Here are several providers worth mentioning:

  • Kasisto uses AI and ML algorithms to power omnichannel virtual assistants.
  • Wells Fargo was the first US bank to launch an AI-driven customer chat experience for Facebook Messenger.
  • Bank of America’s Erica, an AI-based virtual assistant, was launched in March 2018 and helped more than 1 million users in the first three months.

Machine learning is the new superpower on the stock market

How is machine learning used in finance future telling? The vast volumes of trading operations result in tons of historical data — an unlimited potential for learning. Still, historical data is only the grounds on which predictions are made. ML algorithms monitor data sources available in real time, such as news and trade results, to pinpoint patterns indicating stock market dynamics. The task left to traders is to determine which ML algorithms to include in their strategies, make a trading forecast, and choose a behavioral pattern.

A typical workflow for a trading system using supervised learning

(Video) How Banking & Finance Industry use Machine Learning and Artificial Intelligence | Tutort Academy

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Source: A Machine Learning Framework for Algorithmic Trading on Energy Markets

What are the benefits of ML in stock market?

  • The predictive capacities of machines are unlimited, unlike those of a human.
  • Machine learning can detect the slightest indicators of prices going up or down.
  • Machine learning can easily compare data over several decades.
  • Machine learning algorithms can make trading decisions extremely quickly.
  • No bias from human interpretation.

Here are several providers worth mentioning in this sector:

  • Sentient Technologies is an AI company that’s developing and applying proprietary quantitative trading and investment strategies using distributed artificial intelligence systems.
  • Walnut Algorithms is a European startup that offers AI and ML finance solutions for investment management.
  • I Know First is an Israeli company offering stock forecasts based on predictions of machine learning algorithms.

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Read more: Learn about the use of Artificial Intelligence for predictions in financial markets and asset management

Final thoughts

The world of financial services has entered the era of artificial intelligence and machine learning. The number of uses of machine learning in finance is constantly rising. The technology is beginning to play a significant role in various processes, including loan approvals, stock forecasts, and fraud prevention. Yet not many FinTech providers have embraced machine learning as a critical driver for financial services. More accessible machine learning tools, a variety of algorithms, and decent computing capacity will only increase the number of interactions between machine learning and custom software product developmentinFinTech, so it’s high time to catch up with this trend.

Contact experts at Intellias if you plan to develop or scale digital solutions based on recent developments in machine learning.

FAQs

How is machine learning used in finance and banking? ›

Machine learning in anti money laundering enables banks to accurately find the very subtle and usually hidden events and correlations in user behavior that may signal fraud. By automating the complex anomaly detection process, financial institutions can process much more data much faster than human rule-based systems.

How is machine learning used in banking industry? ›

This allows financial institutions to increasingly personalize their offers, increase customer loyalty, and deliver on customer expectations in real time. For example, machine learning-based budgeting tools integrated into mobile banking apps can help customers to make better financial decisions.

How is machine learning used in finance? ›

In finance, machine learning algorithms are used to detect fraud, automate trading activities, and provide financial advisory services to investors. Machine learning can analyze millions of data sets within a short time to improve the outcomes without being explicitly programmed.

Which two artificial intelligence use cases apply to financial services? ›

Machine Learning, predictive analytics, and voice recognition tools are all increasing the value of digital banking services. AI Chatbots, facial recognition banking apps, and fraud detection systems and applications are all a few best examples of AI in banking and finance industry.

How is ML used in Fintech? ›

Machine learning (ML) uses computer algorithms and analytics to build predictive models that can solve business problems, including such from the fintech industry. ML is based on algorithms that can learn from data without relying on rule-based programming.

How is machine learning used in Fintech? ›

FinTech companies can use ML algorithm to predict market risk, identify future financial opportunities, reduce fraud, etc. Companies can train their machine learning models on huge amounts of data such as financial interactions, loan repayments, company stocks, customer interactions, etc.

How is artificial intelligence and machine learning used in banking? ›

“Advanced cognitive technologies such as AI and machine learning are helping banks strengthen their TPRM programs by automating the manual effort, empowering banks to better identify and anticipate risk and more quickly conform to rapidly evolving regulatory requirements.

How do banks use AI? ›

AI is also being implemented by banks within middle-office functions to assess risks, detect and prevent payments fraud, improve processes for anti-money laundering (AML) and perform know-your-customer (KYC) regulatory checks.

How are algorithms used in finance? ›

Financial companies use algorithms in areas such as loan pricing, stock trading, asset-liability management, and many automated functions. For example, algorithmic trading, known as algo trading, is used for deciding the timing, pricing, and quantity of stock orders.

How is data science used in banking? ›

Applying data science technologies like AI, NLP, and machine learning algorithms can help banks in several areas like fraud detection, risk management, customer sentiment analysis, and personalized marketing. Data science is disrupting the banking sector like never before.

Is Python used in finance? ›

Python is an ideal programming language for the financial industry. Widespread across the investment banking and hedge fund industries, banks are using Python to solve quantitative problems for pricing, trade management, and risk management platforms.

Which of the following is a use case for AI in finance? ›

Answer Real Time Questions. In the finance industry, AI can be used to examine cash accounts, credit accounts, and investment accounts to look at a person's overall financial health, keeping up with real-time changes and then creating customized advice based on new incoming data.

What type of AI is used in finance? ›

Alphasense: This is “an AI-powered search engine for the finance industry … [serving] clients like banks, investment firms and Fortune 500 companies,” according to Built In. The platform uses natural language processing to analyze keyword searches and discover trends and changes in the markets.

How AI is used in finance sector? ›

AI in Corporate Finance

AI is particularly helpful in corporate finance as it can better predict and assess loan risks. For companies looking to increase their value, AI technologies such as machine learning can help improve loan underwriting and reduce financial risk.

What is IoT in banking? ›

The Internet of Things (IoT) is a method of collecting and analyzing data. Along with many other industries like manufacturing, insurance, retail, and even agriculture, IoT in banking generates much interest. The banking sector uses IoT to improve customer service, security, and management.

How can AI help fintech companies? ›

Together, AI and ML help lending enterprises identify, sort, and make accurate decisions based on multiple data points, rapidly and simultaneously. The benefits of using such disruptive tech are many, such as faster KYC, prompt arrival at a credit score, swift detection of fraud and risk management, and lower costs.

What are the biggest challenges in implementing artificial intelligence in banking? ›

Among the obstacles hampering banks' efforts, the most common is the lack of a clear strategy for AI. ⁶ Two additional challenges for many banks are, first, a weak core technology and data backbone and, second, an outmoded operating model and talent strategy.

How Data Science is used in fintech? ›

Data Scientists can build models and predictions of the feature changes in customer behavior and possible reaction to the fintech product changes. Process improvement can be based on the usage of the Digital twins' approach, which is a trend of product development for the last few years.

What is IoT in fintech? ›

IoT in Fintech & Banking

IoT has given the fintech industry an interesting boost, especially when it comes to security and payment processing. The Internet-of-Things can be found acting as mobile point-of-sale systems, as well as cybersecurity tools that safely process and encrypt payment information.

How is reinforcement learning used in finance? ›

In market-making the market maker buys and sells stocks with the goal of maximizing the profit from buying and selling them and minimizing the inventory risk. Reinforcement learning has been used successfully to come up with price setting strategies to maximize profit and minimize inventory risk.

What is machine learning with real world example? ›

Image recognition is a well-known and widespread example of machine learning in the real world. It can identify an object as a digital image, based on the intensity of the pixels in black and white images or colour images. Real-world examples of image recognition: Label an x-ray as cancerous or not.

What are the main applications of machine learning ml in business? ›

Here are 10 applications of machine learning in business that are being used to solve problems and deliver tangible business benefits:
  • Real-time chatbot agents. ...
  • Decision support. ...
  • Customer recommendation engines. ...
  • Customer churn modeling. ...
  • Dynamic pricing tactics. ...
  • Market research and customer segmentation. ...
  • Fraud detection.
9 Jun 2020

How is machine learning used in day to day life? ›

Machine learning in such scenarios helps to estimate the regions where congestion can be found on the basis of daily experiences. Online Transportation Networks: When booking a cab, the app estimates the price of the ride. When sharing these services, how do they minimize the detours? The answer is machine learning.

How AI is transforming the banking sector? ›

AI is changing the quality of products and services the banking industry offers. Not only has it provided better methods to handle data and improve customer experience, but it has also simplified, sped up, and redefined traditional processes to make them more efficient.

How artificial intelligence is transforming the banking industry? ›

Even the traditional banks have started to offer more online services. Artificial intelligence streamlines their processes, makes smarter decisions, and manages customer service requests with fewer resources. It also plays a crucial role in risk management by preventing fraud and fighting money laundering in real-time.

Does Goldman Sachs use AI? ›

Goldman Sachs' Marcus is reaping 'massive' returns on its investment in AI chatbot services. The use of AI has helped Marcus streamline how it offers customer support.

Which AI is used for customer sentiment analysis in banking? ›

AI vendor products such as Expert System's Cogito platform provide NLP-based sentiment analysis capabilities. Cogito could give banks the ability to gain insights about customer such as top customer issues from customer survey data.

What are the 4 types of algorithm? ›

Introduction To Types of Algorithms

Brute Force algorithm. Greedy algorithm. Recursive algorithm. Backtracking algorithm.

What are 3 examples of algorithms? ›

Common examples include: the recipe for baking a cake, the method we use to solve a long division problem, the process of doing laundry, and the functionality of a search engine are all examples of an algorithm.

How is Deep learning used in finance? ›

Deep learning models use learned patterns and results of document processing to assess credit risks and loan requests. This data covers income, occupation, age, current financial assets, current credit scores, overdrafts, outstanding balance, foreclosures, loan payments.

Why do banks need data scientists? ›

From huge data, data science helps banks to collect and analyze them. Additionally, it helps to isolate the necessary data from a huge customer data and process it to improve decision-making.

How banks use predictive analytics? ›

Predictive analytics helps banks distinguish between the various portfolio risks effectively, by optimizing the collections process. It helps banks segregate risky customers from the risk-free ones. This can help banks devise actions and strategies to achieve positive results.

Which data is used in banking system? ›

Banks generate various types of data, be it related to customer information, transactional information, financial statements, credit scores, loan details, etc. 2. Velocity: It is related to the speed with which new data is added to the bank's database.

Which banks use Python? ›

Python has been used with success by companies like Stripe, Robinhood or Zopa. According to the HackerRank 2018 Developer Skills Report, Python was among the top three most popular languages in financial services. In 2020 Python still appears to be one of the most wanted languages in the bank industry.

Is R or Python better for finance? ›

Most serious data scientists prefer R to Python, but if you want to work in data science or machine learning in an investment bank, you're probably going to have to put your partiality to R aside. Banks overwhelmingly use Python instead.

Which programming language is best for finance? ›

Java is the top-ranked programming language in finance, according to HackerRank, for reasons that mirror its general cross-industry popularity. The language has a friendly learning curve, can handle significant amounts of data, and boasts rigid security features.

How is AI used in finance? ›

Artificial intelligence (AI) is increasingly deployed by financial services providers across industries within the financial sector. It has the potential to transform business models and markets for trading, credit and blockchain-based finance, generate efficiencies, reduce friction and enhance the product offerings.

How are algorithms used in finance? ›

Financial companies use algorithms in areas such as loan pricing, stock trading, asset-liability management, and many automated functions. For example, algorithmic trading, known as algo trading, is used for deciding the timing, pricing, and quantity of stock orders.

How is machine learning used in accounting? ›

When used as part of financial planning & analysis (FP&A), machine learning can be used to analyze data to define or refine data models used for forecasting. The quality of the data set being used and the risk of inherent biases may again impact the quality of the predictions provided by machine learning.

How is Deep learning used in finance? ›

Deep learning models use learned patterns and results of document processing to assess credit risks and loan requests. This data covers income, occupation, age, current financial assets, current credit scores, overdrafts, outstanding balance, foreclosures, loan payments.

What type of AI is used in finance? ›

Alphasense: This is “an AI-powered search engine for the finance industry … [serving] clients like banks, investment firms and Fortune 500 companies,” according to Built In. The platform uses natural language processing to analyze keyword searches and discover trends and changes in the markets.

Which of the following is a use case for AI in finance? ›

Answer Real Time Questions. In the finance industry, AI can be used to examine cash accounts, credit accounts, and investment accounts to look at a person's overall financial health, keeping up with real-time changes and then creating customized advice based on new incoming data.

How investment banks use AI? ›

Investment banks can use AI in six critical ways:

Algorithmic trade execution. Research recommendations. Operational break and failure prediction. Trade and communications surveillance.

What are the 4 types of algorithm? ›

Introduction To Types of Algorithms

Brute Force algorithm. Greedy algorithm. Recursive algorithm. Backtracking algorithm.

What are 3 examples of algorithms? ›

Common examples include: the recipe for baking a cake, the method we use to solve a long division problem, the process of doing laundry, and the functionality of a search engine are all examples of an algorithm.

Do banks use algorithmic trading? ›

For instance, many banks employ algorithms designed to execute trades without significantly impacting market prices. Although certain types of algorithmic trading may reduce perceived bid-ask spreads, algorithmic trading also increases operational risk at individual firms and across the financial system.

What accounting problems can be solved by machine learning? ›

Accounting processes such as expense reports, accounts payable, and risk assessment may be easily automated using machine learning.

Why is AI & machine learning good news for the accounting and finance industry? ›

One of the main ways that artificial intelligence can help the financial and accounting industry is by reducing human errors. Much of the standard data-entry practices that are common-place in accounting could be replaced with machines. Machines could manage invoices and low-level bookkeeping tasks.

How machine learning is disrupting the accounting industry? ›

Machine learning algorithms will process and review the data, recognise anomalies and compile a list of outliers for auditors to check. Instead of spending most of their time checking data, auditors can apply their skills to investigating and deducing the reason behind a pattern or anomaly.

Why is machine learning important in finance? ›

The benefits of using machine learning in finance include:

Reduced costs by automating routine processes. Increased revenue through faster and better decision-making. Improved customer experiences by automatically prioritizing important issues. Augmented security through faster fraud detection and suspicious activity.

Is machine learning the future of finance? ›

Machine learning is a beneficial tool that can be used in the future of finance. It can both reduce costs and increase profits for banks, which is what they want.

How do banks do forecasting? ›

Banking forecasting using machine learning allows companies to monitor incoming transaction parameters in real-time. The algorithm examines the time series, evaluates customer actions, and examines other variables to determine how likely a suspicious transaction is to be fraudulent.

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