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Leveraging Machine Learning and AI in Finance: Applications and Use Cases

AI and ML in Finance

The finance industry has been one of the earliest adopters of AI. With its first entrance into the finance landscape going as far back as the 1980s, AI is driving some of the biggest trends in finance, such as fraud detection, algorithmic trading, banking chatbots, etc.

Financial businesses worldwide are working overtime to integrate these features into their operations. As a result, the job market for AI and ML aspirants interested in finance is enormous.

Keep reading to understand the impact of AI in finance through key examples and use cases. We will discuss how an industry-recognized online AI ML program can help you fast-track your career in this exciting field.

The Growing Impact of AI and Machine Learning in Finance

The financial sector has witnessed a massive adoption of AI for back-end and front-end operations. In the 2021 McKinsey report, about 85 percent of business experts acknowledged AI as a necessity. The reasons were obvious — financial businesses using AI saved more than 20 percent in costs and had a revenue boost of more than 10 percent.

Meanwhile, ML implementation in the global finance market is estimated to increase by a CAGR of 22.5 percent to touch $38.13 billion by 2030. Almost 27 percent of businesses worldwide increasingly use ML for multiple tasks, such as enhancing portfolio optimization and process workflow.

This is only the beginning. With future developments in AI and ML, the financial sector is poised to become more data-driven to improve its customer service, daily operations, financial projections, and strategy.

Also Read: Machine Learning in Healthcare: Applications, Use Cases, and Careers

The Role of AI in Finance

Financial services companies use AI for primary and complex operations. Let’s dive into some of the leading applications of machine learning and artificial intelligence in finance.

Automation of Daily Activities

Banking services aims to save almost $70 billion by 2025 by using AI to automate daily activities, such as accounts payable, financial close processes, and customer services. The firm Oracle uses AI to close its books 50 percent of the time as its competitors in the S&P 500.

Identifying and Tracking Unusual Transactions

About 58 percent of the banks attested to a substantial use of AI for fraud detection. A great example is the top financial organization, JPMorgan Chase, which uses AI to send details of every credit card payment conducted anywhere in the world to the central data centers for fraud evaluation.

Customer Service

Financial services customers are sensitive about receiving transaction confirmation and fraud alerts. They also require updates about interest rates and subscription price bumps. AI can work as a conduit by assessing the customer history and sending the relevant information based on the data input from the professionals. MasterCard integrated an AI chatbot into its Facebook Messenger, enabling several Facebook users to connect and enquire about the services directly.

Apart from these, there are other applications such as:

  • Risk assessment
  • Risk management
  • Idea generation
  • Portfolio construction
  • Customer churn assessment
  • Data management
  • Personalization of services

A reputed online AI ML course will train you in the skills required to implement AI in several more applications in the financial sector.

Also Read: What is Machine Learning? A Comprehensive Guide for Beginners

The Role of Machine Learning in Finance

A data-driven sector like finance requires constant data collection, analysis, and extrapolation to plan for future strategies. Services such as credit bureau firms, digital wealth management, financial analytics, and credit assessment require tools to scour through the data and draw actionable inferences.

As a result, ML has multiple applications in the finance sector. Here are three of the most common ones.

Fraud Detection

ML algorithm helps evaluate the financial history and patterns and raises an alarm when an unusual transaction occurs. For example, the ML-based CPP Fraud Analytics software improved the detection rates to more than 50 percent and reduced the investigation duration by 70 percent.

Asset Management

ML can collate the massive amount of data generated in asset inventory, long-term funding, and life cycle costing. The monetary transactions are analyzed to predict future possibilities and liabilities. For instance, Goldman Sachs, the global financial leader, uses ML solutions to identify investment and wealth distribution opportunities by predicting future trends.

Trading Predictions

The stock brokerage firms use ML to scrutinize stock growth and fall trends using data spanning several years. The multiple learning models help in programmed iterations to arrive at the final prediction of the stock prices. The investment platform Kavout is a splendid example of such predictive analysis. The platform utilizes ML models to assess SEC filings, sentiment analysis, and price patterns from analyst reports, social media, news, and blogs. It then provides actionable insights on the stock prices on its dashboard that stock brokers can readily access.

Other applications of machine learning in finance include wealth management, economic trend analysis, portfolio diversification, investment advisory, and options pricing, to name a few.

Key Challenges Regarding Machine Learning and AI in Finance

Despite the numerous applications, implementing AI and ML faces certain roadblocks. Here are some of the most common challenges of implementing ML and artificial intelligence in finance.

  • Insufficient data: AI and ML work on the principle of data scrutiny. Thus, the models cannot provide accurate numbers and predictions if the data are scarce or unreliable.
  • Repetitive data cleanup: No data can be used as it is. They have to go through mathematical functions to become usable. The usual algorithms have this step as an integral part of the process. However, it cannot be directly input into the algorithm when fresh data is obtained. The professional has to repeat the cleanup process and modify the algorithm every time.
  • Insufficient transparency: AI and ML seem like magic when one just types in the query, and the algorithm spouts an answer. Yet, it is difficult to verify the answer’s accuracy due to the algorithm’s complexity. It also results in errors in the algorithm going undetected.
  • Multiple data formats: ML and AI work on data from multiple sources spanning continents and time zones. As a result, the units, currencies, formats, file types, etc., may differ even if the required data type is the same. Thus, getting the data and input them straight into the algorithm becomes difficult.
  • Model training: A restricted amount and form of data can constrain the development of the learning models. Especially in the supervised learning model, limited data are used for analysis, while the remaining data work as the control values. Sometimes, the models may be adjusted to match the control values for greater accuracy. However, these adjustments may make the algorithm unworkable for practical cases.
  • Lack of human experience: All said and done, AI and ML are machines, after all. They provide answers based solely on the input. Even if the answer seems correct, it lacks the element of human experience that can decide how to implement it in a universally desirable manner.

Nevertheless, these challenges do not hamper the general application of AI and ML in the industry.

Also Read: Machine Learning Interview Questions & Answers

Top Use Cases of Machine Learning and AI in Finance

Do AI and ML sound exciting so far? If yes, dig your heels as we review the top use cases that will convince you of the amazing possibilities for artificial intelligence in finance.

Document Processing

The pandemic brought the need to conduct maximum financial business online. It forced financial institutions to contact customers via online forms and video calls for document submission and processing. In fact, finance processes form almost 41 percent of the AI use cases worldwide. A typical use case is onboarding a customer for a new bank account.

The customer is asked to upload the requisite documents. AI uses test and image recognition to extract the essential details and automatically load them into the form. The data from the documents may be structured or unstructured. AI examines the data and proposes options for credit cards and loans. Once the customer onboarding is complete and the transactions from the customer commence, ML can help analyze the patterns and suggest the best investment options for the customer.


Cybersecurity is of utmost priority in the finance sector. Roughly 33 percent of the AI use cases globally are for improving financial firms’ cybersecurity. Suppose a broking firm uses AI and ML to monitor the transactions throughout the organization. AI helps take regular monitoring operations off the backs of human professionals. Any hint of a breach will warrant immediate action from AI.

Further, ML models help assess the historical data related to the transactions of each account. If there is a sudden large investment or a sharp drop in the share prices, the ML tool will compare it to the control values and alert the firm to unusual activity.

Credit Intermediation

Credit intermediation is when a company or individual acts as an intermediary between the consumers and the lending firms. For instance, say a consumer approaches an intermediary to help get a house loan. The intermediary obtains bank statements and other financial documents from the consumer. They then use AI to extract the information and implement ML models to analyze the credit situation. The ML models may also predict the spending patterns of the consumer. The AI algorithm then provides the loan amount, interest rate, and the most suitable options for lending firms. This reduces human bias and rejection of loans due to incomplete data.

Multi-lingual Customer Service

In a multi-national bank, the customers may not always be conversant with English. For instance, a customer from Germany approaches the bank with issues regarding credit card payments. The bank then implements AI chatbots that converse with customers in German and direct them to the correct webpage or customer service contact details. The customer talks in German to the customer service agent, who receives a simultaneous AI-translated script. The customer uploads relevant documents that may be in German. The AI uses image and text recognition to extract information and translate it into English for the agent, who then advises the customer with the appropriate solution.

Customer Retention

A credit card company is assessing its customer base to identify customers at risk of non-payment and loss of credit card privileges. It employs ML models to comb through the vast number of customers and transactions. The models are designed to categorize the customers within specific parameters, such as number of non-payments, time required to reach the credit limit, etc. Once such customers are isolated, the company then uses AI to reach out to them and offer repayment options, budget planning, or automated warnings for repayment.

Master AI and ML Skills for a Successful Career in Finance

AI and ML are rapidly evolving fields, and there are many applications of ML and AI in finance. If you have a knack for financial services and computer science, you can combine your interests and build a rewarding career. Our online AI ML bootcamp can help you start on the right foot.

Besides learning the fundamentals of generative and explainable AI, data preparation and model development, and learning and regression models, you’ll also get to handle real-world projects that will equip you with job-ready skills.

You might also like to read:

How to Become an AI Architect: A Beginner’s Guide

How to Become a Robotics Engineer? A Comprehensive Guide

Machine Learning Engineer Job Description – A Beginner’s Guide

How To Start a Career in AI and Machine Learning

Career Guide: How to Become an AI Engineer

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