The world of finance is going through a paradigm shift. With the proliferation of more digital devices, a slew of data generated promises the creation of new possibilities. When paired with the advancements of artificial intelligence (AI), it has been transforming the financial industry through automation. Today, AI is reshaping the way financial institutions generate and use data insights. This usage of intelligent algorithms to offer improved financial operations and solutions has resulted in the genesis of fintech. It is a portmanteau developed using the words “financial” and “technology”.
Fintech helps firms manage their financial practices more effectively. Financial incumbents that offer services in banking, insurance, risk management, and trade are all using fintech to enhance their operations. It must be evident by now that fintech is not powered by AI alone. Technologies like blockchain, robotic process automation, and big data analytics also contribute to the fintech movement. Crowdfunding platforms, mobile payments, cryptocurrencies, blockchain, insurance, and budgeting applications are all examples of fintech which are now in use.
Meanwhile, AI is helping fintech firms receive a considerable amount of funding too. This is because AI has a lot of untapped potentials, which continues to draw investors.
How AI Algorithms Help Fintech?
The adoption of AI algorithms has moved from the periphery to the central part of fintech. With data as a core component, the disruptive nature of AI brings enormous potential gains. Fintechs are already looking into better methods to leverage data and AI in their services and products so that they can provide relevant, trustworthy insights, suggestions, and controls. One of the primary uses of AI in fintech is to find anomalous transaction patterns in data that may indicate the presence of fraudulent activities. Prior to the AI-based approach, financial institutions leveraged a rules-based approach, which requires manual work and human supervision).
To give a fraud index to every particular payment process, the rule-based approach employs logical assertions and weight summation. Once the aggregate of these scores is calculated, the size of the score defines the risk score. Although this approach works, it also creates a lot of false positives for new forthcoming legitimate vendors and false negatives if a large firm commits fraud every now and then. Also, this method consisted of a series of long-term processing and multiple verification steps, all of which were detrimental to the user experience.
AI Algorithms in Fraud Detection
Today, businesses employ supervised and unsupervised machine learning to train models so that they can detect fraud attempts faster than they can use human (rule-based) methods. Some of the widely used algorithms include:
This method uses a cause-effect relationship and is employed while making a categorical choice on structured data sets. Based on the given set of parameters that are checked and, on the basis of the probability calculated, users can conclude if a transaction is ‘fraud’ or ‘non-fraud.’
This is a technique for classifying data items using decision trees, where each decision tree checks for different conditions. The model chooses a variable that allows for the best record splitting and then repeats the procedure. When a transaction request is fed to the model, it checks for transaction parameters like amount, IP address, date, time, location, card details, frequency of transaction. These parameters are used as variables for distinct decision trees. Trees vote to determine if a transaction appears to be fraudulent, and the model makes a consensus decision.
Other examples of AI algorithms in fraud detection include Naive Bayes, Neural networks, Support Vector Machines, Linear Discriminant Analysis, boosted classifiers, bagged classifiers, and more.
Fintech also uses chatbots that specialize in addressing consumer queries regarding their current balance, previous spending, and transaction history. Chatbots convert complicated business interactions into simple chats using natural language processing, conversational AI, and machine learning technologies. Some chatbots provide answers to client queries about investments, trends, savings, loans, and insurance plans, among other things, to help customers keep good track of their financial situation.
In general, these chatbots are given input comprising keywords and sentences containing the relevant phrases that will be processed by NLP during the training phase. For instance, for a chatbot feature offered by a bank, the chatbot training data would contain the normal queries that come while making a transaction like, amount to be sent, payment status, etc. When data is acquired from customers, it is tagged with NLP annotation services to make the key phrases comprehensible to computers. This allows computers to learn from the conversation and respond appropriately.
Common AI Use Cases in Fintech
KYC and Identity Verification
Earlier, humans had to manually verify if the given documents of customers are accurate or not. This tedious task used a lot of time and resources. Now, AI can assist banking applications and other online financial services in automatically and securely verifying clients’ identities. This is referred to as KYC (Know Your Customer). For example, to prove identification online, companies can use computer vision and cross-check if the input picture or a snapshot of the ID card of a customer is authentic.
Product Recommendation via Risk Assessment
Financial institutions like banks can leverage AI for the profiling and assessment of clients based on their risk scores. Through automated machine learning, artificial neural networks, these firms can assess borrowers with little to no credit information or history. By analyzing thousands of data points, lenders can get transparency that conventional underwriting systems cannot. This allows them to properly categorize groups as per their “at-risk” levels. Then based on this classification process, advisors can opt to connect financial products with each risk profile and offer them to customers via targeted and automated recommendation systems. By more accurate prediction of risks, such profiling work helps lenders cut losses significantly.
Fraud Detection and Prevention
In spite of advances in the fintech business, frauds have long been a source of concern, particularly in the financial and banking industries. To mitigate this, fintech firms use AI tools to gather evidence and analyze data, where they study and monitor user behavior patterns to spot fraud attempts and incidents that are unusual. With their self-learning abilities, if any suspicious activity is detected, AI algorithms can not only alert companies about potentially fraudulent transactions but also enable taking steps to avoid them. In case a pattern deviates from the confidence band, the issue is assigned to a human employee who could make a rational decision. Such use of AI can also help government bodies identify corruption networks.
AI is already playing a significant role in the FinTech sector, and it will continue to do so in the future. While its existing algorithms cater to a wide range of activities, they are continuously learning from massive amounts of data and closing the gap by moving the industry closer to a totally automated financial system. Hence, companies are investing in AI-powered fintech to augment their financial capabilities and increase their ROI.