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AI in Finance? Use Cases, Benefits, and Challenges





AI in finance? If you’re unfamiliar with this combination, chances are you are missing out on a lot. The main goals of financial institutions  – banks, hedge funds, and insurance companies – are minimizing risks, reducing costs, and providing high-end customer services to clients using AI.

With vast amounts of data in the financial sector, it becomes increasingly important to use AI for data analysis, risk management, personalized service, and managing portfolios. According to a survey in 2023 done by NVIDIA on 200 financial institutions based in America and Europe, the companies were working on the following use cases:

  • 26% of them were working on a large language model
  • 23% on Recommender System
  • 23% on Portfolio Optimization
  • 22% on Fraud Detection

Half of the respondents believed that AI would enhance their annual returns by 10%, and one-third estimated that AI would reduce their annual expenses by 10%

In this blog, we will learn about AI use cases in finance, its benefits, and the challenges financial institutions face while employing AI.

AI in Finance

AI is a combination of data, computational power, and technology.  The technological aspect of AI is as follows:

Machine Learning: Machine Learning consists of algorithms that can be trained on financial data, either supervised or unsupervised, for classifying, predicting, and finding anomalies in financial data.

Deep Learning: Deep Learning employs neural networks to analyze financial data. Deep Learning is suitable when we have billions of records for market data.

Natural Language Processing: Natural Language Processing in finance is used to analyze and extract information from contracts, sentiment analysis of financial markets, and enhance the customer experience in fintech using chatbots.

What are the use cases of AI in finance?

Fraud Detection

Fraud in banking and finance not only causes financial distress but also affects the image of the institution. AI is trained on historical data and can differentiate a typical transaction from an anomaly. As more data and research methods become available, the accuracy of AI systems to detect fraud will further increase.

Risk Assessment

Risk assessment is essential in checking loan eligibility and the probability of a borrower defaulting on a loan. AI can analyze credit records and financial statements to assess borrowers’ risk profiles. Moreover, auditors can use AI to examine financial records to ensure that the company complies with applicable laws and regulations.


AI is trained on years of financial data and can identify trends that can be difficult to see with the naked eye. Put simply, AI generates better trading signals. AI can be used in high-frequency trading, where trades are taken in split seconds on the slight fluctuation of prices. Portfolio management companies can develop AI systems to gain higher rates of return which will gain clients’ trust and, in turn, bring more business.

24/7 Customer Service

In banking, AI-driven chatbots can provide 24/7 customer service by answering common questions. With the advent of ChatGPT, there is no denying the business potential of large language models.

What are the benefits of using AI in Finance?

Saves Time

AI in finance saves time by automating repetitive tasks, freeing humans to handle complex issues. Auditors don’t have to read a company's financial records while auditing. Moreover, utilizing customer support chatbots in fintech apps saves time, speeds up processes, and provides 24/7 service.

Saves Money

AI helps in fraud detection, portfolio management, and risk assessment while lending loans. Thus, using AI can assist a financial institution in making well-informed decisions that minimize losses.


Human error in the financial ecosystem could have adverse effects. AI systems are efficient in complex decision-making, reducing the risk of human error.

Challenges of using AI in finance

Data Quality

Garbage In, Garbage Out. Creating an effective data strategy for a financial institution requires due diligence. Identifying and vetting data sources, collecting them, and converting them to the required form can be challenging for AI-driven financial sectors.

Data Privacy and Security

Financial institutions use personal data every day. Therefore, it is essential that they should adopt security measures to keep personal data private. Moreover, they should follow data regulation laws to understand the lawful usage of data.


Gnostic behavior towards variables such as color, race, ethnicity, or gender is called bias in AI. Historical training data may have biases that can translate into AI systems. Biased applications can be harmful:  limitations in lending loans to a minority group. Risk assessment and management are necessary for an unbiased ai application.

AI in Finance- Way Forward

AI in finance can enhance customer experience, detect fraudulent transactions, assess risks, helps in making trading strategies for hedge funds, and whatnot. The AI ecosystem (applications and research methods) is continuously evolving, and clients lean towards hassle-free experiences. Financial institutions should constantly update their AI systems based on their customer needs and cutting-edge AI use cases available.

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