AI in FinTech has seen some important developments in the past few years. As a result, AI technology is quickly changing the way the industry operates.

FinTech agents and some traditional financial industry players have gotten stronger throughout the pandemic crisis. Many financial companies have been affected, but many more are quickly adapting to offer financial services that are adapted for the world’s new reality.

Some companies in the finance industry had already been strengthening their business models with state-of-the-art and innovative HiTech solutions even before the pandemic situation started. This process has now been accelerated. In particular, the use of Artificial Intelligence (AI) and Machine Learning (ML) is redefining how many things are done within the financial industry.

As more financial activities are done through apps, companies can obtain powerful insights through new data points. This, in turn, allows new disruptive technologies to create many opportunities for users and companies alike.

However, almost everyone thinks that AI and ML are only for big companies with tech experts and large pools of capital. Nothing can be farther away from the truth. FinTech companies large and small are using these technologies, paired up with powerful apps, for all sorts of purposes.

We’ve already discussed the differences between AI and ML, and also some of the uses of ML. In this post, we discuss some of the most important ways in which FinTech companies are using AI and ML. Hopefully, you can get some inspiration from them for your business.

AI in FinTech: Use Cases of Artificial Intelligence and Machine Learning

Financial technology is nowhere near to replacing human intelligence, but it can surely augment its powers. By using computer-based tools that rely on Big Data analytics, financial firms can harness the power of tools like an Artificial Neural Network or other disruptive tools to build powerful products and decision-making solutions to innovate in terms of financial services. This is generating important changes both at an organizational and human scale.

AI in FinTech has the potential to help companies achieve their growth objectives, gain a competitive advantage, and make them more relevant to their clients. Additionally, it can also help them reduce operational costs and make internal processes more efficient. Users can benefit from this through better personal financial management.

These are just a few examples of the most important uses of AI and ML algorithms in finance.

  • Improved financial decision making
  • Security & fraud detection
  • Asset management
  • Customer support
  • Insurance
  • Loans
  • Forecasts
  • Personalization

Improved Financial Decision Making

FinTech apps are developing new and interesting ways in which users can process information. Thanks to the power of data science and visualization tools, analyzing data through apps becomes easy, transforming it into digestible insights. As a result, users can make use of complex information to improve their financial decision-making.

Security & Fraud Detection

As digital transformation processes take over the world, financial cybercrimes will also grow. The silver lining to it is that thanks to AI and ML, companies and users are now able to secure themselves and their accounts.

Cryptocurrencies and blockchain are often associated with financial cybersecurity. However, in the near future, we will also associate AI and ML with digital security and anti-money laundering solutions. Algorithms are capable of detecting suspicious activity, and even better, they can notify users. These technologies can continuously monitor unusual patterns, so there is no need to be vigilant 24/7. Users can keep track of everything that goes on behind their backs while being confident that their assets are safe.

There has also been a great impact on behalf of these technologies regarding the detection of other illegal activities like money laundering. Governments and other institutions have the power, thanks to AI and ML, to use an army of bits and bytes to trace corruption networks.

Asset Management

Investment funds have been using complex algorithms for a while now to develop robust forecasts and simulations. Thanks to this, the world of asset and wealth management has been able to restructure many of its processes and offer new services like wealth management tools. FinTech firms have taken notice of this and are implementing these solutions into apps so that users can take advantage of them.

App users can now manage bank statements and make important transactions directly from any of their devices. Most importantly, thanks to AI and ML solutions, users have the choice to do so reducing the number of intermediaries. As a result, wealth management has been able to remove unnecessary processes, helping reduce operational costs.

Customer Support

Bots are one of the most famous AI applications. Although they have been around for some time, only recently have they started to get traction thanks to ML algorithms. We are now seeing the rise of potent chatbots that can interact with customers to produce an immediate response to a number of customer requests.

FinTech companies are using bots as a major channel to solve customer issues. Robo advisors and automated customer support are some of the most common ML solutions. Results have been impactful as chatbots allow companies to reduce costs and increase customer satisfaction.

As physical distancing becomes the new normal, financial institutions will opt more and more for this type of technology to solve customer issues, improving the Customer Experience along the way. Brick and mortar offices are not expected to disappear anytime soon, but they will most likely be relegated for specific activities.


One of the most innovative ways in which AI and ML are being used is to reshape how insurance policies are evaluated. Because this industry is heavily driven by financial tools, FinTech apps are being used to determine risk levels. Companies can calculate someone’s level of risk through their activity.

This has been used with success by the auto industry. A combination of IoT technologies and FinTech app development has opened up for this industry the possibility to calculate a person’s risk level by assessing their driving skills through a mobile app.

Smart contracts that use technologies like Blockchain and AI are also being used to innovate within the insurance industry.


This is probably the most popular way in which FinTech companies are benefiting from HiTech. The world has seen a wave of money lending apps thanks to the possibility of using someone’s financial habits and credit exposure to calculate their credit scoring, making the underwriting process more efficient without the need for human intervention.

Loans through AI and ML can be done in a faster way while reducing inefficiencies. Additionally, they tend to be more accurate than the traditional underwriting process thanks to an improved client risk profile approach. Some experts even argue that this might help customers by reducing biases that can occur through human decision-making.

Although this last is true, the opposite, negative biases, can also occur. Agents that use these mechanisms need to make sure that they have everything worked out in terms of calculating credit scoring, otherwise, they risk segregating an important pool of users from their services.


I’ve already mentioned how HiTech and data science tools have been used by financial companies to improve their predictive analytics. However, it is worth mentioning that this technology is now at the service of ordinary people like you and me.

Apps have the power to help users perform robust calculations on important matters like their spending habits at a very low cost and in a personalized way. Making use of consumer insights obtained through key data points, apps can help throughout the entire process of analyzing data to produce powerful predictive analytics. This aids users to keep track of their spending and calculate whether they will meet their financial goals.


This leads us to the last item on our list. Although this might seem obvious, it is an important way in which FinTech companies are using AI and ML together with Natural Language Processing. The combination of these technologies, together with powerful apps, has given companies and users the possibility to personalize finances.

One of the most successful products in this category is smart wallets, which allow users to manage their finances in new and customized ways. What used to be a rigid industry is now breaking outdated stereotypes to deliver a customized User Experience.

AI in FinTech: Wrapping It Up

Artificial Intelligence and Machine Learning are two potent HiTech tools that are having an important impact on a human scale. The FinTech industry and traditional financial firms understand this, and that is why they are using them for everyone’s benefit.

AI in FinTech has the potential to augment human intelligence for better financial decision-making, and it can also improve internal organizational processes. This has an important impact when it comes to the Customer Experience.

At Koombea we expect the implementation of technologies like an Artificial Neural Network in the finance industry to increase. We will definitely see a computational arms race in the coming years as businesses evolve and new models are created. This process will go hand in hand with the evolution of powerful apps. As HiTech solutions evolve, so will apps, and this will open up many possibilities for new and powerful financial services.

FinTech and traditional finance apps can benefit from implementing disruptive AI technology that uses Machine Learning to strengthen their competitive advantage. Remember that to succeed in implementing AI in FinTech, it is best to partner with an app development company that knows the intricacies of the financial world.


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