Artificial intelligence is a modern technological marvel. AI has led to several service improvements in various industries. At its core, AI is the ability of computers and computing devices to reason and act like humans. This means AI gives computers the ability to perform functions or tasks done by humans.

AI is in the finance sector such as banks, hedge funds, etc. The implementation of AI in financial services dates back to the 1980s when some fortune 1000 companies used it.

Contrary to the 1980s, AI in the finance sector is more common. The deployment of AI in financial services has offered many advantages to companies. However, the use of AI in finance is still a complex concept for some. In this article, we highlight how AI in finance works.

Machine Learning Model

In all areas where it has been implemented, the backbone of the AI system is a machine learning model. Machine learning models offer the AI system its perceived intelligence. 

In financial services, there can be different types of machine learning models. The models differ depending on their intended purposes. In banks, the most common ML model is one used to determine whether a client requesting a loan will default. Historical data is used to train these models.

For determining the possibility of defaulting, the models use historical data. This allows the algorithms to note patterns and trends of defaulters. Once the model is developed by an ML engineer or data scientist, it now has to be deployed to the host system. ML models can be deployed to web apps, computer software, mobile apps, etc. 

Model deployment is an integral part of integrating AI into financial services. This is why it is vital to consult professional agencies that will help manage the ML ops and model. 

The use of AI for determining loan risk has been embraced by banks and even microlending agencies. AI in financial services can be used for various purposes.

Uses of AI in Financial Services

1. Risk Management

In finance, risk is considered an integral aspect to consider when trading. In the recent past, more companies have embraced the use of machine learning models to identify trends in markets and therefore make predictions.

Accurate predictions of market trends using AI have helped several companies mitigate losing huge sums of cash. The use of AI to mitigate trade risks can also be used to maximize trading profits. By predicting market trends, financial institutions can know the optimal times to initiate a trade, therefore maximizing profits. 

2. Automated Trading

The benefits of AI in finance for trading go beyond helping businesses to mitigate risks. AI can also be used in quantitative trading. Quantitative trading entails using quantitative algorithms and methods to make trading strategies.

With AI, computers can analyze large datasets, popularly referred to as big data, to extract insights. The implementation of AI in financial services allows computers to analyze datasets faster and more accurately than a human being would have done.

Once the insights are extracted from the data, the insights can be used to develop algorithmic processes for trading. This can be used to automate certain trades. This will allow financial institutions to make more trades and save time, thereby increasing their productivity and overall profits.

3. Personalization of Banking Services

Conventional banking offered all clients the same services and banking benefits. This method of banking proved to be inefficient when some banking institutions adopted the use of AI.

In banks, AI systems can analyze customer data and cluster them based on their profiles. This allows the bank to know its clients better. Once the bank is aware of its client’s preferences and service needs, the bank offers more personalized services to its clients. 

The personalization of banking services is evident in how banks offer several loan options to business owners. The loan options are often tweaked to offer payment plans depending on one’s business revenues. The loans also allow business owners to change their payment plans if business conditions change.

AI systems also offer personalized help to clients through the use of chatbots. Chatbots use natural language processing capabilities to interact with the bank’s clients and offer the needed assistance. 

An advantage of chatbots over customer support personnel is that a chatbot can handle more clients at a time. This improves the client’s satisfaction with the bank’s customer support services. The AI chatbots can also offer clients financial advice that is personalized based on their banking data.

Furthermore, chatbots will be available to answer client questions at any time of the day. This means that clients will be tended to even if it’s past the bank’s working hours.

4. Detection of Fraudulent Transactions

The rapid development of internet technologies has also brought about a significant problem for financial institutions. Financial fraud rates increased significantly as internet technologies got improved.

AI helps financial institutions to detect and stop fraudulent transactions before completion. This is done using ML models. The machine learning models are taught to identify fraudulent transactions using the historical data of such transactions. Once trained, the models identify fraudulent transactions using the transaction data.

AI fraud detection systems work faster than humans and with higher accuracy due to their ability to note minute transaction details. 

5. Banking Safety

In line with current trends, banking institutions have developed mobile applications. The applications allow clients to make bank transactions without visiting an ATM or going to the bank’s offices.

Mobile banking applications rely on AI systems heavily to improve their security. The first instance of relying on AI for banking security is the use of biometric data for logging in. Several banking applications use face identification or fingerprint identification for clients to log in and make transactions.

AI systems can also detect when a client’s mobile application has been hacked and can stop any transactions. The AI systems observe data such as device location, withdrawal amount, etc., to notice any deviations from the client’s regular use. 

Once the AI system notes a significant deviation from regular application use, it halts the transaction. This helps prevent cases of fraud through a hacked mobile banking application.


AI systems are at the helm of many functions in the financial services industry. The number of functions using AI in financial institutions is expected to increase. As a business offering financial services, you must adopt AI in your functions to enjoy the wide array of benefits it offers.

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