The cutting-edge technologies of the new age are changing the landscape of many industries. Specifically, artificial intelligence and machine learning are bringing about a lot of changes in the environment surrounding us. Both the technologies are transforming the banking sector as well for better experiences. Following the highly impressive implications these technologies, more and more banks are adopting AI/ML technologies.
A survey report presented by Narrative Science and the National Research Institute revealed that 32 percent of financial services executives assured that they have already started using AI solutions including predictive analytics, recommendation engine, and voice recognition.
But one aspect that is creating hindrance in complete adoption of AI is the legacy system. Leaders are resisting the upgradation of current technology processes because it is a more traditional industry. These legacy system place obstacles in seamless AI integration process.
Since banking is a more traditional industry, leaders are reluctant to upgrade or change current technology processes. The problem is these legacy systems often prevent seamless integration of AI.
However, to stay relevant to the competitive time, banks need to deploy financial technology solutions to their processes. The needs of consumers and their demand from the bank are increasing and AI can promisingly deliver to that.
Also, machine learning technology enables banks to stay less dependent on human expertise which implies that human staff can focus more on improving customer experience.
Here are the five areas where the AI/ML technology can improve the banking experience substantially.
Revamping Traditional Credit Scoring Process
Artificial intelligence-based credit scoring can work more efficiently than traditional processes by enabling fast, accurate assessment of the potential borrower. These technologies can further remove biases as well. With the help of AI, banks can determine which person has a higher default risk and who is more creditworthy without even checking the extensive credit history.
Additionally, ML models can perform credit scoring over and over again to learn from mistakes and make improvements when it comes to handling a huge financial dataset. Henceforth, the consumer will receive an early response from banks and understand their finances better.
Managing and Mitigating Risks
Bank can utilize automated credit risk testing to better manage and mitigate risks after getting accurate reports without human error. Also, by analyzing the history of risk cases, AI can aid banks to predict several issues and stay risk-ready.
By analyzing a vast amount of data which humans cannot do in a short time, AI algorithms can reduce risk assessments to shorter time span say few minutes.
Substantially Preventing Frauds
Almost every financial institution is infected by fraud cases. This is where AI/ML technologies come into the picture. These technologies by analyzing spending patterns, location and client behavior can recognize abnormalities and alert the cardholder which consequently helps in reducing frauds.
AI/ML enabled systems can not only track suspicious behavior but also if provided with additional information can block the transaction within a few seconds. This boon of AI centric fraud detection approach can help banks catch frauds in real-time rather than wasting time in taking steps to rectify the issue.
Offering More Personalized Experience
Banks can offer a better and more personalized customer experience with the virtue of AI and ML. Consumers and businesses want more in less and that when they acknowledge the value of unique experiences and better options.
ML algorithms can analyze data of individual consumer and monitor abnormalities. It can also notify members if their card was charged more than one time for a single expense.
ML models can also forecast which banking tools might be used by a consumer and further recommend them in order to make them take better decisions.
Automating Tasks for Better Service
Freeing up human resources and capacities by automating repetitive and usual tasks can add to better customer service. Using RPA technology, financial institutions can remove human error and re-architect workforce tasks.
ML algorithms can use image recognition to recognize patterns in the banking cum legal agreements which would take around 360,000 labor hours per year with complete human involvement.
Usage of chatbots can avail quick and reliable information to customers.
Undoubtedly, AI and ML adoption by financial institutions are driven by consumer demand. For bank customers, having a secure and personalized experience has become their basic need. Since banks must depend on customer loyalty, they need to embrace AI/ML technologies to deliver better solution and services.