AI in FinTech has seen some critical developments in the past few years. As a result, AI technology is quickly changing how the industry operates, particularly in processing financial transactions.
FinTech agents and some traditional financial industry players have strengthened throughout the pandemic crisis. Many financial companies have been affected, but many more are quickly adapting to offer financial services adapted to 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 started. This process has now been accelerated. In particular, Artificial Intelligence (AI) and Machine Learning (ML) are 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 some of the uses of ML. In this post, we discuss some of the most critical ways FinTech companies use AI and ML. Hopefully, you can get some inspiration from them for your business.
Introduction to AI in FinTech
Artificial intelligence (AI) is transforming the financial industry in unprecedented ways. The integration of AI in financial technology (FinTech) has given rise to a synergy that streamlines traditional financial processes and introduces groundbreaking capabilities. AI in FinTech involves using technologies like machine learning to enhance various tasks within the finance sector, including improving efficiency, accuracy, and speed in activities such as data analysis, forecasting, investment management, risk assessment, fraud detection, and customer service.
By leveraging AI, financial institutions can process vast customer data to uncover previously unattainable insights. This not only helps in making more informed decisions but also in predicting market trends and customer behaviors with greater precision. The result is a more agile and responsive financial sector that can better serve clients’ needs.
AI in FinTech: Use Cases of Artificial Intelligence and Machine Learning
Financial technology is nowhere near 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 artificial neural networks (AI) or other disruptive tools to build robust products and decision-making solutions to innovate in financial services. This is generating significant 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 become more relevant to their clients. Additionally, it can 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 exciting ways for users to 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 use complex information to improve their financial decision-making.
Security & Fraud Detection in Financial Transactions
As digital transformation processes take over the world, financial cybercrimes will also grow. The silver lining is that thanks to AI and ML, companies and users can now secure themselves and their accounts.
Cryptocurrencies and blockchain are often associated with financial cybersecurity. However, we will shortly associate AI and ML with digital security and anti-money laundering solutions. Algorithms can detect 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 behind their backs, confident that their assets are safe.
There has also been a significant impact on behalf of these technologies regarding detecting other illegal activities like money laundering. Thanks to AI and ML, governments and other institutions have the power to use an army of bits and bytes to trace corruption networks.
Asset Management
Investment funds have been using complex algorithms to develop robust forecasts and simulations. Thanks to this, the asset and wealth management world has been able to restructure many of its processes and offer new services like wealth management tools. FinTech firms have noticed this and are implementing these solutions into apps so users can take advantage of them.
App users can manage bank statements and make essential transactions directly from any device. Most importantly, thanks to AI and ML solutions, users can choose to reduce the number of intermediaries. As a result, wealth management has removed 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, thanks to ML algorithms, they have only recently started to get traction. We are now seeing the rise of potent chatbots that can interact with customers to produce an immediate response to several customer requests. AI tools analyze customer data to deliver tailored financial advice, enhancing customer satisfaction and engagement.
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. The 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 increasingly use 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 to specific activities.
Insurance
One of the most innovative ways AI and ML are used is to reshape how insurance policies are evaluated. Because financial tools heavily drive this industry, FinTech apps are used to determine risk levels. Companies can calculate someone’s level of risk through their activity.
The auto industry has used this with success. A combination of IoT technologies and FinTech app development has allowed this industry 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.
Loans
This is probably the most popular way FinTech companies benefit from HiTech. Thanks to the possibility of using someone’s financial habits and credit exposure to calculate their credit scoring, the world has seen a wave of money lending apps, making the underwriting process more efficient without human intervention.
Loans through AI and ML can be made faster 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 ensure that they have everything worked out in calculating credit scoring. Otherwise, they risk segregating an essential pool of users from their services.
Forecasts
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 noting that this technology is now available to 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. Using 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 in keeping track of their spending and calculating whether they will meet their financial goals.
Personalization & Tailored Financial Advice
This leads us to the last item on our list. Although this might seem obvious, it is an essential 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 personalized User Experience.
Benefits of AI in FinTech
The use of AI in FinTech brings numerous advantages. AI improves decision-making accuracy and promotes continuous growth, crucial in transforming the industry. AI can automate repetitive tasks such as document processing and data entry, reducing the risk of human errors and ensuring data accuracy and reliability. This automation frees human resources to focus on more strategic activities, enhancing operational efficiency.
AI-powered customer service can provide personalized interactions and recommendations, improving customer satisfaction and loyalty. By analyzing customer data, AI systems can tailor financial advice to individual needs, offering a more customized experience. Additionally, AI can help financial institutions make informed decisions and reduce potential losses by analyzing vast amounts of data and identifying patterns and trends. This capability is precious in fraud detection and risk management, where timely and accurate insights are crucial.
Challenges and Limitations of AI in FinTech
As with integrating any emerging technology, FinTech organizations may experience some challenges when adopting AI. Data privacy is a significant challenge for Fintech’s use of AI as it deals with sensitive financial data. Ensuring any AI tools used employ robust data protection measures is crucial to prevent data breaches and unauthorized access. This requires continuous monitoring and updating of security protocols to stay ahead of potential threats.
Regulatory and compliance issues can also pose challenges for FinTech companies using AI, as the rapidly evolving nature of AI technologies can outpace existing regulatory frameworks. Financial institutions must navigate these complexities to remain compliant while leveraging AI’s benefits. Furthermore, the need for skilled personnel and technological infrastructure is another challenge FinTech companies may face, as adopting and maintaining AI systems can require expertise in machine learning, data science, cybersecurity, and other digital skills. Investing in training and development is essential to build a workforce capable of managing these advanced technologies.
Future of AI in FinTech
Experts predict many innovative ways companies can use AI in FinTech. Combining AI and blockchain to enhance security in transaction processing, pairing AI and quantum computing for faster and more complex data analysis, and creating robo-advisors to democratize access to financial advice are just a few examples. These advancements promise to revolutionize the financial sector further, making it more efficient and accessible.
As AI in Fintech continues to evolve, it is crucial to recognize and address its challenges and risks. Financial institutions and FinTech companies must balance utilizing AI to enhance services and safeguard customer information. Transparency and fairness in AI algorithms are crucial to mitigate the risk of bias and discrimination. Regular audits and adherence to ethical guidelines can help identify and rectify biased outcomes, ensuring that AI systems operate fairly and equitably. By maintaining this balance, the financial industry can harness AI’s full potential while protecting its customers’ interests.
AI in FinTech: Wrapping It Up
Artificial Intelligence and Machine Learning are two potent HiTech tools that have an important impact on a human scale. The FinTech industry and traditional financial firms understand this, which is why they use them for everyone’s benefit.
AI in FinTech has the potential to augment human intelligence for better financial decision-making and improve internal organizational processes. This has an important impact on the Customer Experience.
At Koombea, we expect the implementation of technologies like Artificial Neural Networks in the finance industry to increase. We will 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, opening 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 implement AI in FinTech successfully; it is best to partner with a development company that knows the intricacies of the financial world.
