AI in Finance? Use Cases, Advantages, and Challenges


AI in finance? For those who’re unfamiliar with this mix, likelihood is you’re missing out on loads. The predominant goals of monetary institutions  – banks, hedge funds, and insurance firms – are minimizing risks, reducing costs, and providing high-end customer services to clients using AI.

With vast amounts of knowledge within the financial sector, it becomes increasingly necessary to make use of AI for data evaluation, risk management, personalized service, and managing portfolios. In response to a survey in 2023 done by NVIDIA on 200 financial institutions based in America and Europe, the businesses were working on the next use cases:

  • 26% of them were working on a big 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 cut back their annual expenses by 10%

On this blog, we are going to find out about AI use cases in finance, its advantages, and the challenges financial institutions face while employing AI.

AI in Finance

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

Machine Learning: Machine Learning consists of algorithms that could 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 research financial data. Deep Learning is suitable when we’ve billions of records for market data.

Natural Language Processing: Natural Language Processing in finance is used to research and extract information from contracts, sentiment evaluation of monetary markets, and enhance the client 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 additionally affects the image of the institution. AI is trained on historical data and might differentiate a typical transaction from an anomaly. As more data and research methods develop into available, the accuracy of AI systems to detect fraud will further increase.

Risk Assessment

Risk assessment is crucial in checking loan eligibility and the probability of a borrower defaulting on a loan. AI can analyze credit records and financial statements to evaluate borrowers’ risk profiles. Furthermore, auditors can use AI to look at financial records to be certain that the corporate complies with applicable laws and regulations.


AI is trained on years of monetary data and might discover trends that could be difficult to see with the naked eye. Put simply, AI generates higher trading signals. AI could be utilized in high-frequency trading, where trades are taken in split seconds on the slight fluctuation of costs. Portfolio management corporations can develop AI systems to realize higher rates of return which is able to gain clients’ trust and, in turn, bring more business.

24/7 Customer Service

In banking, AI-driven chatbots can provide 24/7 customer support by answering common questions. With the appearance of ChatGPT, there isn’t any denying the business potential of huge language models.

What are the advantages 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 need to read an organization’s financial records while auditing. Furthermore, utilizing customer support chatbots in fintech apps saves time, accelerates 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 within the financial ecosystem could have antagonistic effects. AI systems are efficient in complex decision-making, reducing the danger of human error.

Challenges of using AI in finance

Data Quality

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

Data Privacy and Security

Financial institutions use personal data each day. Subsequently, it is crucial that they need to adopt security measures to maintain personal data private. Furthermore, they need to follow data regulation laws to know the lawful usage of knowledge.


Gnostic behavior towards variables akin to color, race, ethnicity, or gender is named bias in AI. Historical training data can have biases that may translate into AI systems. Biased applications could be harmful:  limitations in lending loans to a minority group. Risk assessment and management are mandatory 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 constantly evolving, and clients lean towards hassle-free experiences. Financial institutions should always update their AI systems based on their customer needs and cutting-edge AI use cases available.

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