The banking landscape is just beginning to change. Financial institutions need to use cutting-edge technology to optimize processes and meet the latest market demands. But the implementation of technologies in banking is still in its early stages. However, banks using AI and ML are quickly going to overtake their competitors. So far, technology has been the most affected on security systems and streamlined customer services. And these are keys to business success in this area.
Let’s see what you can get from implementing technologies in the banking area.
Chatbots for Customer Support with NLP
Conversational AI technologies for banking are widespread nowadays. Chatbots are becoming more popular in providing businesses with high customer engagement and reducing the burden on operators. Accordingly, in this way, it reduces the engagement of human resources. But are they now perfect? Not always.
In recent years, the banking industry increasingly implements NLP chatbots and has focused on customer experience. It allows for providing a conversational banking system with a high degree of personalization. These chatbots meet the customers’ needs in a minimal period of time, remembering previous experiences. Data analysis helps to determine the similarity of requests and gives the advantage of anticipating the client’s need and solving his problem correctly. Using the customer sentiment analysis model (NLP) can provide the most correct interaction with the client, satisfying his needs and leaving only positive emotions. It is the most successful example that helps financial institutions improve customer service.
Customer Segmentation for Personalized Offers
Customer segmentation gives you an advantage in understanding every customer. Also, it is useful for aligning relevant strategies and tactics to meet their distinctive needs and receive more profits. You can reach out to customers in the most fitting way, having as a basis their interests, spending habits, budgets, preferences, and buying patterns. And when you interact with customers based on these things, they believe you care. This naturally increases their level of trust and satisfaction with your services.
Using clustering as a machine learning technique, you can do this automatically and with large volumes of data. Receiving analytical data helps to make subsequent decisions and further implement your strategy. The most common are usually personalized offers about different types of cards and their benefits, based on the previous activities of the client. But the strategy of personalized cashback offers is becoming the most popular now. People spend more than they planned to satisfy their needs and at the same time, they are satisfied with your services.
Forecasting Customer Demand for ATMs and Optimizing Their Operation
Automated Teller Machines (ATMs) provide many distinct conveniences: cash in hand, account balance information, deposit capabilities for cash, and other financial transactions. But the main advantage is that they are found almost everywhere.
Relative to the location of the ATM, you can predict the demand for withdrawing cash and bills, which are most common in the region. For example, if an ATM is located near a shopping center, it is logical to assume that there will be the most frequent withdrawals of large amounts of bills. If the ATM is in a residential area, large bills are not so often used. In this way, it is possible to predict the periodicity of filling the ATM with cash and to optimize the circulation of bills in relation to demand.
We can collect ATM transaction data and analyze it to create an optimal ATM refill strategy. This will create convenience for users and prevent situations when cash runs out. Clients receive convenient service and the bank has an advantage over competitors.
More Effective Fraud Detection
Fraudulent activities include money laundering, cyberattacks, fraudulent banking claims, forged bank checks, identity theft, and many such illegal practices. Organizations implement different fraud detection and prevention technologies and risk management strategies to combat growing fraudulent transactions for banking.
There are many techniques that are broadly categorized as statistical data analysis techniques and artificial intelligence or AI-based techniques. The most common are data mining, neural networks, machine learning, pattern recognition, face recognition, and others. It gives banks an advantage to use predictive analytics to create a fraud risk score. Along with this, they can continuously monitor transactions, social networks, and high-risk anomalies, and apply behavioral analytics to enable real-time decision-making. Technologies can help financial institutions automate and improve effective security systems, fraud detection, and prevention.
Verification of Creditworthiness
Thanks to rapidly increasing computing power, machine learning plays a vital role in credit risk modeling. Previously, there were fewer factors that were taken into account when issuing a loan. The complexity nowadays is that banks incorporate many dimensions they examine during credit risk assessment. These additional circumstances typically include other financial information such as liquidity ratio, or behavioral information such as loan or trade credit payment behavior. Summarizing these various dimensions into one score is challenging, but machine learning techniques help achieve this goal.
Rather than hand-coding a specific set of instructions to accomplish a particular task, the ML algorithm uses large amounts of data to learn how to perform one or the other task. This greatly simplifies the process of calculation of creditworthiness, given the variety of given data and different combinations. The main advantages of this approach are the accuracy and processing of large volumes of data in a short time.
Customer Detection and Recognition
Physical security in banking has evolved toward network-based solutions with high-resolution cameras and high-capacity recorders. But with machine learning, we can get even more out of surveillance cameras inside banks. By creating a system for recognizing the face of a client entering the bank, it will be possible to determine who it is. If this is a bank client – the nearest operator will automatically receive information about this user, his cards, account status, and credit history. If it is not a client of the bank, the system detects it as unknown.
ML learns every time on new data, which gives an advantage to creating large systems of client bases. Accordingly, the security system will be at the highest level, since some information will already be known about each person who enters the bank. It will also show which persons (unknowns) the guards should look more closely at.
Conclusion
Technologies do not stand still. But in the sphere of banking, their potential is not fully realized yet. Data analysis, artificial intelligence, machine learning – all these technologies can improve the banking system and raise it to a new level. It will be convenient and effective, so you should not delay the introduction of new technological solutions. Contact the Amazinum team to get advice on how we can develop your business and how you can get more profit from it.
