The fintech (financial technologies) industry is one area where AI can be and is already being used very effectively. Given the vast amounts of customer, transaction and market data, the financial industry can benefit from the use of AI and machine learning in many areas of its value chain. From lending to investing to customer service, many data-driven companies have already proven that AI can add real value across a wide range of areas in the FinTech industry. Here are 10 interesting examples of how AI can be used to solve many everyday problems faced by FinTech companies.
Rank 10 - Fraud detection with network analysis in banking
Given the not even remotely traceable mass of transactions in modern banking, fraud has become a major problem for financial institutions. To effectively reduce the number of fraudulent activities, banking institutions monitor the relationships between their customers, products and transactions to identify suspicious networks and patterns. By integrating different data sources to create a database of customer activity and information, the data can be logically linked and transformed. Normally, this data would be spot-checked to detect fraudulent activity. But this is time-consuming and slow. With the help of machine learning and its pattern recognition capability, this Automated process and unusual activities, relationships and transactions can thus be uncovered. To make this data even more accessible, a visualisation tool is implemented to show the fraudulent activities. This reduces not only the Number of fraud casesbut also helps in the process, Real-time customer and transaction relationships to present. More on this in our Case Study.
9th place - Robo-Advisor-supported portfolio management
Traditionally, the general public has had very limited access to financial advice from private bankers and investment firms and no experience in performing financial analysis. Robo-advisors act as Cost-effective alternative for financial adviceby eliminating the need for human effort and making investment information accessible to everyone. The tool assesses the client's financial goals, current state, expected returns and risk appetite using an online questionnaire. By performing market analyses based on quantitative and machine learning models and using large volumes of historical data the robo-advisor can then issue investment recommendationsto achieve a diversified portfolio taking into account the Client preferences to create. This way, clients not only have access to investment advice around the clock - sound investment advice can also be offered to a broad mass of people.
8th place - Claims processing with NLP
Because written claims are often used in the insurance industry, the claims department must review them to determine whether the customer is also entitled to compensation. This time-consuming process involves at least one person per claim, which can be a huge amount of work given the often small amounts of claims. With the customer data available, such as contract data, damage information in a structured format and a description of the damage, NLP can be used to solve this problem. A claims classification model is then trained to classify different claims and identify missing information. With this approach, the Settlement time reduced from several days to minutes and the time needed to check damage reports can be significantly reduced. More on the settlement of small claims here.
Rank 7 - Data-supported credit assessment
A typical use case for AI in the financial industry is credit scoring using machine learning. A common problem for financial companies is overdue loan interest based on inaccurate risk assessments. Because typical credit risk scores only use data from the customer's financial past, they cannot accurately reflect the customer's creditworthiness. By incorporating transactional data, social media data, employment data and financial data, an ML model is trained on past data. With this trained model, the Credit risk assessed in seconds and the best credit solution is recommended. In this way, the credit company not only has its Loan rates around 90% lowered, but can also More flexible loans to clients awarded.
Rank 6 - Sentiment analysis in retail with NLP
Numerous factors influence the stock market - including people's moods. They play a crucial role in stock market movements because market trends change quickly with mood. With the help of natural language processing (NLP), we can Data from social media or annual reports scraped and analyses be trained. By training a model with market-related text data, stocks are classified into three categories: positive, negative and neutral. This information can later be Help with important trading decisions and predict share prices in different market scenarios.
Rank 5 - Chatbots for a better customer experience
With the rise of new start-ups and companies offering unconventional digital financial services, customers are flocking to challenger banks like Chime or frictionless service providers like Venmo. Many new fintech companies have developed a product offering that targets the points in the value chain where the weaknesses of incumbent banks are greatest. One very common factor is low satisfaction with customer service. This problem, which is common in many industries, can be easily addressed with machine learning. Through the Evaluation of frequently asked questions and the creation of a question catalogue, a chatbot can be implemented in web and edge services to answer frequently asked questions and address typical customer concerns. Since the chatbot Around the clock is available via a messenger-like interface, the customer can get immediate answers to their questions and concerns. This significantly reduces customer churn because it creates a frictionless, real-time customer experience.
Rank 4 - Analysis of late payments with predictive analytics
Debt is on the rise - both among companies and private individuals. Unfortunately, not all borrowers can meet their obligations, which then leads to a debt collection process. Because a major reason for credit losses is an often outdated, reactive collection system, AI can significantly improve this system. With the help of machine learning, borrowers can be more accurately classified into different categories and industries. Through data analytics and predictive analytics depending on past cases, the system can Status of the borrower determined become. With this information and a further analysis can be used to identify which borrowers are likely to be able to resolve their arrears themselves and which need loan restructuring or modified payment terms. Read more in our Case Study.
3rd place - Accelerated customer onboarding through AI
The first impression is often the most important one - this is not only true in a personal conversation. When signing up for a new bank account, the process is often impersonal, time-consuming and not very customer-friendly. Customer abandonment is a common problem in the onboarding process and can be remedied by implementing a unique, customer-focused onboarding experience. With the help of OCR and Computer Vision important documents such as the identity card can be identified and compared with the person's face in a video. This not only reduces the need for a "real" interaction between customer and bank, but also also speeds up the onboarding process for new clients. In addition, changes to the user interface can be assessed to improve and customise the user experience and reduce the risk of customer abandonment.
Rank 2 - Customer segmentation based on customer activities and products
Financial institutions work with an enormous number of customers - considering that almost everyone has their own bank account. Therefore, it is almost impossible to manually analyse the relationship between financial products and different customer segments. Using different data sources such as product data, customer data and customer activity, machine learning can identify different customer segments. By displaying these groups on dashboards, valuable insights into product usage can be gained, Cross- and upselling opportunities and gain customer lifetime value. Important customer groups are identified and treated accordingly. The product groups can be assessed for their effective target groups.
Rank 1 - Fraud prevention in the financial sector
Analysing the probability of fraud is a difficult and time-consuming task for financial service providers. In order to ensure that a person is eligible for the credit applied for, salary slips are often requested. These have to be manually checked for plausibility, which is very time-consuming and prone to errors. Modern analytics and machine learning can speed up and simplify the credit checking process for both the customer and the financial service provider. By training a model with past data and using personal data, financial product data and payroll records, fraud can be detected and effectively prevented in a split second. The time needed to manually check payrolls can thus be significantly reduced, resulting in an faster machininga better customer experience and, above all, a clearly lower probability of fraud leads.