Artificial Intelligence in Fintech, explained

Companies in the financial sector can use Artificial Intelligence (AI) to analyze and manage data from multiple sources to gain valuable insights. These innovative results help banks address the challenges they face daily in delivering everyday services such as payment processing.

At present, artificial intelligence is playing a crucial role. It is helping fintech companies automate routine procedures and improve outcomes on a scale beyond human intelligence. The early application of artificial intelligence enables fintech companies to identify threats, prevent fraud, automate everyday tasks and enhance the quality of service. All these lead to improved efficiency and higher profits.

# What do AI and fintech mean exactly?

Fintech (financial technology) is technology-enabled innovation in the financial sector. This can range from managing financial transactions to fighting fraud, as we already wrote here. In recent years, the staggering rise of fintech companies has offered consumers personalized ways to manage their hard-earned money in ways that were not possible a decade ago. AI-powered chatbots, for example, are being used by fintech companies of all sizes, from customer service agents to salespeople.

Indeed, the rise of financial technology has been significant in recent years. In fact, traditional banking has come to know of disruptive fintech companies and their innovative solutions, introducing their technologies that use artificial intelligence and robotics to reduce costs and address consumer pain points.

# The benefits of AI for your business

The similarities between artificial intelligence and fintech are enormous. Improved knowledge and social capabilities have pushed AI technology from the edge to the center of the debate. It brings back the personal touch to banking, which has been somewhat lost in recent decades due to the dominance of multinational banks. Below is a description of how artificial intelligence in the financial sector can improve banks' business processes.

# 1. Data automation/credit risk assessment

AI-based credit scores are perhaps the most promising and relevant. In short, credit scoring is an evaluation of how well a customer can pay and is willing to repay debts.

AI decisions on credit scores are based on a lot of data, such as total income, credit history, transaction analyses, work experience, etc. Credit scoring is a mathematical model based on statistical methods that consider a large amount of information. The result is that credit scores using artificial intelligence provide sensitive, individual credit score assessments based on several additional real-time factors, potentially allowing more people with incomes to access finance.

# 2. Detecting banking fraud

Data is analyzed using AI-based analytics tools to detect suspicious transactions that may indicate fraud. Through artificial intelligence, users' behavior patterns can be monitored, and actions that deviate from the normal can be identified and could show fraud attempts or incidents. These cases are then passed on to human staff for consideration, saving them a great deal of time.

# 3. Increasing safety

Artificial intelligence in the financial world is the driving force behind many solutions to improve security. For example, banks offer apps that can only be accessed with face or fingerprint recognition. This is primarily made possible by artificial intelligence.

Artificial intelligence in fintech offers solutions based on behavior and could revolutionize the financial world. Artificial intelligence can monitor how a customer handles his transactions and determine his typical behavior. Suppose a customer tries to withdraw €5,000 from his account several times from somewhere-not-a-typical-location; artificial intelligence-driven learning can detect and block these activities as possible fraud.

# 4. Automated customer service

Customer service and relationship management is another critical area where artificial intelligence technologies provide tangible value. Fintech companies can use chatbots to quickly answer customer questions and improve customers' overall experience with their products and services. These chatbots allow customer service to be tailored to people and provide expert advice at a low cost.

In addition, chatbots can be available 24/7 and never take days off. Virtual assistants can also be augmented with artificial intelligence to help the customer navigate the bank's offerings, enhance user data and provide personalized calls to action to increase targeted conversions. Virtual assistants and chatbots help fintech companies save time and money on customer service. This will also help improve the quality of artificial intelligence through its various capabilities.

# 5. Analysing user behavior

Artificial intelligence in fintech can predict a user's behavior by using an interface to integrate various artificial intelligence and machine learning techniques, allowing users to gain detailed insights into their data. These are also called AI APIs and can be used to the advantage of banks and fintech companies.

How did we  analyze and optimize spending habits for Bittiq

For example, the customer requests data on his expenses in the past month - a single request. The company uses AI to predict his follow-up request on the server-side and provides this information in the same response. In this way, the company minimizes the number of requests and the burden on the business system. The customer also benefits, as the system works faster when the predictive analysis is correct.

# 6. Financial forecasts

The use of artificial intelligence in finance means that it can identify, analyze and adjust changes in data patterns on its own. Moreover, even if data quality is far from perfect, artificial intelligence in fintech can still come up with valuable insights into the exchange rates, investments, short- or long-term trends predictions.

Even more impressive is the ability of AI to analyze unstructured data, which is essential for financial services. In fintech, this can be applied in various ways, including predicting product suitability and adapting marketing strategy by analyzing customer behavior.

# Examples of use

Artificial intelligence has several practical applications in the fintech sector and throughout the financial world, and even in the broader world beyond. The general essence of artificial intelligence is that it solves problems; it enables companies to save both time and money.

In many cases, the practical use of artificial intelligence has to do with data and enables companies to analyze that data efficiently and cost-effectively. Organizations, especially financial institutions, often have large amounts of data about their customers. Still, they do not usually do anything with it because it takes a lot of time to go through and analyze that data. This is where AI comes in, as artificial intelligence and machine learning effectively analyze large amounts of data in real-time.

A specific example of applying artificial intelligence with data is for banks to decide whether someone is creditworthy. Banks want to offer credit, but they also want to charge the right price. They can avoid overcharging reliable customers and undercharging risky customers by looking at their credit scores. Artificial intelligence lets them look at their own customer data and draw conclusions. From these portfolios, artificial intelligence can derive different kinds of connections. For example, job and address are obvious sources of information, but there is also the argument that even details such as what someone's email provider is could show more or less trustability.

Another way artificial intelligence data analysis can be used is for fraud detection and prevention. As mentioned above, artificial intelligence and machine learning solutions can react in real-time to the data presented to them, find patterns and connections, and may be able to identify fraudulent activity. As one can imagine, this is hugely useful for the financial world, as an incredible number of digital transactions take place every hour, making increased cyber security and successful fraud detection a necessity. Artificial intelligence takes most of the work away from fraud analysts, allowing them to focus on higher-level issues, while artificial intelligence works in the background to identify the smaller problems.

One example of how artificial intelligence can detect fraud is by detecting anomalies. In the bank scenario, someone may have tried to apply for 10 identical loans in 5 minutes; a computer with artificial intelligence could detect this as an anomaly and consider it suspicious. The computer has a basic sense of what is 'normal' and when something deviates from this, it is able to identify and assess it.

Another application of artificial intelligence is automated customer support. Nowadays, people are used to chatting boxes appearing at the bottom of the screen when they visit different websites, and these are of course chatbots with artificial intelligence support ready to help. Companies can simply upload their most frequently asked questions and tell the chatbot which answers to give and also instruct it to refer the customer to someone else for more complex issues. Being able to answer frequently asked questions about the company or the product/service it provides ensures a better experience for the customer as he gets the answer to his question immediately. In addition, the company saves time and money by not having to employ someone to type out answers, or by having an employee focus on something else.