With the evolution of banking apps and fintech firms, brick-and-mortar banks are struggling. Customers expect user-friendly and on-demand features from their financial partners. As a result, there is a heated competition between nonbank alternatives and banks. Statistics say that 5.6 million Americans will switch banks in the coming year. The market conditions demand improved customer experience. Big data can be leveraged for this purpose. This would be possible by machine learning solutions that can design new strategies, deploy purpose-built best practices, and identify customer needs.
The implication of machine learning is vast, though most banks are in the early stage of adoption. 32% of financial services are using recommendation engines or predictive analysis already, according to a survey. One obstacle in the universal adoption of machine learning is legacy systems, as banking is a traditional industry reluctant to upgrade. Machine learning can help deliver what consumers want from banks. In this article, we will see how.
Learning Of the Blog
- ML and Banking
- What’s in for customers?
- Future of Banking
Before we go deep into the banking sector, it is essential to know the basics of machine learning. If you are new to this field, machine learning training by Global Tech Council will do the needful.
ML and Banking
Machine learning algorithms use substantial data sets for analysis and prediction. Implementing ML into larger systems makes it capable of planning and solving problems at scale. With improved machine learning algorithms moving the process into the mainstream, banks are significantly improving customer service and delivering exceptional customer-driven experiences. Machine learning algorithms and advanced analytics are being used to make instant decisions using real-time data. This helps in performing intelligent and sophisticated functions associated with human thinking, unlike predetermined responses. Machine learning can create more informative, efficient, and secure banking experiences by analyzing and combining information from various data sources-increasing a bank’s profitability, and delivering high value to the customer. IDC believes that about 5.6 billion dollars would be spent on ML-enabled solutions by 2022, including automated threat intelligence, prevention, and fraud analysis systems. Also, according to IHS Markit, banking and AI would account for $300B by 2030. These investments would fuel ML innovation and transform the customer journey across the banking industry. The widely available and powerful ML tools have allowed the entry of a change in the industry from commercial to retail banking.
What’s in For Customers?
Customers are looking for easy-to-use and intelligent digital banking products. Providing these products can drive usage patterns and customer engagement, generate more data regarding preferences, and more. ML algorithms train the models on this data to provide improved experience and products. Banks need a strong ML strategy because the banking industry has a lot to gain. Here is how financial firms are using ML algorithms to their advantage:
Faster Credit Decisions
Historically, customers had to wait weeks for a loan application approval. Now the timeline has been reduced to days. More than 60% of customers wish to get immediate responses to sales questions. TechRadar notes that this has formed the basis of an ML-driven application assessment. Machine learning tools can reach an unbiased decision faster than humans by evaluating multiple credit factors. Forbes said that JPMorgan saved more than 360,000 hours of work by analyzing documents in seconds. ML-based credit scoring systems are more sophisticated, accurate, fast, and less expensive. There is no chance of bias because of the objectivity of the machine. A data science expert can train the model based on historical data and help consumers better understand their finances.
Improve Authentication
The rapid adoption of digital banking technologies comes with forgotten account details and lost passwords. ML-based virtual assistants provide speedy customer experience by having passwords reset quickly. Some banks are deploying voiceprint – and ML-driven biometric solutions to authenticate customers using only their voices. HPE’s Deep Learning has helped companies use data analysis for real-time and reliable results. An automated credit risk testing system mitigates risk as the reports received are accurate, free from human error. With the history of risk cases, ML can help with forecasting issues, and the bank can take early steps to avoid problems. Risk assessment can be done in minutes by analyzing enormous amounts of data. Big data can also help a machine learning expert in assessing risk.
Fraud Prevention
For financial client satisfaction, adequate security is paramount. 84% of customers can shift if defense lacks. Emeri noted that banks are now using machine learning tools to identify typical customer behavior and critical deviations to notify potential fraud staff. A tool called Splunk can manage Big data and improve information security best practices without compromising privacy. The result of this data security effort is fewer fraudulent transactions and improved customer satisfaction. ML can substantially impact frauds and inform the cardholder by analyzing location and spending patterns. No human can flag suspicious behavior and block transactions entirely within seconds like an ML-enabled system.
Predicting Consumer Demands
Predicting what clients want is the ultimate customer experience outcome for banks. ML tools can analyze massive data sets across categories like demographics, buying patterns, service requests, and transaction volumes. The analysis can help banks create targeted loans, savings, and credit offers, which are of high value to the clients but offer low risk to the financial institutions. ML can help banks stay ahead in the competition in terms of predictive personal interactions and service response. A personalized approach is the new normal because banks are expected to deliver new banking methods to their loyal customers. This technology offers an edge to both consumers and service providers.
Future of Banking
Banks can deliver customer-oriented experiences because of the availability of ML-based solutions. The application strategy and methodology is based on business needs. ML will continue to benefit the banking industry. However, the full potential can only be unleashed when the infrastructure supports the data needs and technology. There are new interconnectivity and regulatory demands because of the reliance on ML. If you are interested in this field, take up a machine learning certification course or AI ML certification today!