According to studies, predictive analytics in the banking industry will result in a growth of $5 billion by the year 2026, and an increased CAGR of 20% from 2016 to 2026.
Retail banking is an incredibly effective vehicle that drives customer satisfaction by producing benefits like secure deposits, provides low-cost funding, offers budget-friendly credit services, and is insensitive toward interest. Being an integral part of every sector, predictive analytics has seeped into the world of banking and enhanced its functionality by improving fraud detection, introducing chatbots, enhancing credit decisions and loan management, and managing risks.
Predictive analytics in retail banking can allure potential customers and retain the existing ones, forecast possible threats, and provide respective solutions –
- Client segmentation – Identify customer segments with respect to their behavior.
- Credit scoring – Offer ideal lending decisions based on the client’s creditworthiness.
- Fraud detection – Pinpoint potential risks and take measures to curb them.
- Risk management – Detect suspicious activity and manage them through AI implementation.
- Sales – Bestow existing customers with reformed and more advanced services.
- Loans – Optimization of loan origination processes.
- Spend behavior – Understand customer spending behavior and offer rewards respectively.
- Customer churn – Identify causes of customer churn and take appropriate action to retain.
Predictive Analytics Presents Retail Banking Solutions
Predictive analytics has reformed the sector of retail banking. Witnessing a spike in growth and other business benefits, the upcoming years will see more and more banks depending on predictive analytics. Data analytics presents us with the following retail banking solutions –
- Delivering a holistic framework of evolving customer needs, leading to the development of necessary services and strategies.
- Providing a synchronized fashion of communication from all platforms for a better clientele experience.
- Innovating better and more proficient business models and infrastructure that can undertake the changing needs of customers and boost satisfaction.
- Implementing personalized marketing strategies by segregating customers based on demographic details and transaction behavior, helps banks identify the needs of valuable customers.
Benefits of Predictive Analytics in Retail Banking
Predictive analytics analyze historical data to make organized decisions for the future. This can play a major role in the digital banking industry – understand customer needs, proffer tailor-made experiences, create new business models, and habituate with new technology.
One of the leading advantages of predictive data analytics services is its ability to detect cyber fraud. In the era of advanced science, cybercrime sees a steep increase. To keep confidential information safe in banks from such malicious activities, a growing population of institutions is taking help from big data tools. Several initiations are made to enhance KYC (Know Your Client) verification procedures, and anti-fraud solutions that can detect and investigate criminal acts to curb them.
Credit scoring is based on predictive analytics. It prognosticates a user’s credit behavior by analyzing their credit reports. With credit scoring, it becomes increasingly easier to make decisions swiftly, for instance, increasing or decreasing the value of a loan. Thus, it is highly important to one’s financial health.
Data analytics in retail banking prioritizes engagement. It uses advanced algorithms to understand the depths of customer requirements, thereby improving customer acquisition. It can also identify reasons for customer switches, which in turn, can reduce customer abandonment. This strategy is used to create loyalty programs, which further increase retention. Data analytics in retail banking has benefited many businesses.
Risk management has become extremely robust with evolving times. It helps mitigate and solve risks and other suspicious activities. With the help of AI tools, banks can now make rapid decisions and predict data regarding delinquency and pricing. It can also pick on the creditworthiness of clients, which further helps in understanding clients’ lending behavior. This will not only help solve potential defaults but also retain existing customers.
Data analytics is known for reducing manual efforts and increasing automation. With a depletion in human intervention, manual errors, time and extra costs go down. It automates routine tasks like credit scoring, increasing the profitability and productivity of banks.
Cross-selling is a sales technique that analyzes consumer psychology and concludes which product will sell the most based on the current market requirement. The strategy of cross-selling works the best when multiple products are up for grabs. By tracing customer needs, it will strengthen customer relationships and trigger profitability.
Data science has made a lot of positive contributions in the field of retail banking. According to reports, the use of virtual assistance has improved the speed of interaction and reduced customer wait time. Such assistance gets smarter with every client conversation, and can gradually offer guidance to customers to boost their financial health, provide details regarding past transactions, and manage money over text messages. It is projected that by the end of 2023, chatbots and virtual assistants will aid banks to save up to $7 billion worldwide.
Predictive analytics and data analytics in retail banking is definitely altering the finance scenario. It is boosting accuracy, cost savings, risk management, and the ability to make better decisions. At Josh, we offer the perfect data analytics solutions for your banking business. We facilitate hassle-free solutions, help in making improved choices, and enable you to identify and curb potential risks. Through predictive analytics services, we ensure businesses can forecast customer behavior and potential threats, and implement the necessary actions to yield the best results.