By Suresh Ramamurthi
Each year, financial institutions adopt new innovations to continue appealing to consumer demands for faster, simplified service experiences. A 2018 executive survey by NewVantage found that nearly 80 percent of top executives fear that their firms are at risk of disruption and displacement from highly-competitive, technology-driven firms. This percentage represents a sharp jump from the 47 percent in NewVantage’s 2017 survey. A nearly unanimous 98.6 percent of surveyed executives indicated that their firm aspires to create a data-driven culture.
Though the motivation is there, financial institutions—especially community banks—still struggle to overcome various challenges that hinder innovation efforts. Some of these challenges include rigid internal management, the lack of an innovative culture, outdated technology or employee training, and a general lack of clarity on where to innovate.
As the chairman and chief technology officer of a small, century-old community bank in Weir, Kansas, I am aware of the challenges faced by my peers. At CBW Bank, we’ve embarked on a modernization initiative that includes online banking, mobile banking and remote deposit capture. With rapidly evolving technologies and regulations, we expect to continue to develop and launch products and services that use innovation to serve our customers better.
As financial institutions settle into 2019, it will only become more important take advantage of technology to achieve faster and more focused innovation.
Using machine learning to efficiently manage risk
Thanks to the conception of digital banking channels, financial institutions today have an extensive amount of customer data readily available to them. This data provides insights that can be channeled into risk management efforts. To do so, banks must first dispose of any inconsistent or isolated data systems to ensure real-time, accurate access to customer data.
As digital banking grows and transforms all areas of business, banks must approach tighter compliance requirements and risk management in a new way.
Disposing of disparate data systems helps create one centralized system that allows banks to organize data in a measurable, real-time view. This data has the potential to create immediate risk-scoring. Banks have begun to harness this instantaneous access to data to evaluate financial activity for risk. Risk levels can then be assigned to various types of financial activity using previously calculated models. The combination of tighter requirements and the development of new technology has given rise to a new era of risk management using machine learning.
Machine learning provides banks the ability to design and manage high-quality, self-learning behavioral models. The system can sort through large data sets to identify complex and varied patterns, creating accurate risk models that improve over time. This risk management approach will make institution operations more efficient, cost effective and compliant. Machine learning also allows banks to create new products and services with compliance already built in, thus allowing them to innovate more efficiently without the worry of regulatory strain.
Improving the customer experience with machine learning and AI
Machine learning provides various benefits for banks beyond compliance regulation. It enables them to learn more about their customers and drive new revenue opportunities. Digital access to real-time data provides insight into customer behaviors, interactions and preferences.
As the industry develops a stronger grasp on machine learning, more and more financial institutions will begin using it to identify and process relevant customer information, using the predictive technology to anticipate customer needs. This not only improves customer relationships, but also provides insight as to where innovation would be most valuable within the institution.
Artificial intelligence (AI) is also revolutionizing the ways financial institutions discover new revenue opportunities. Data generated through machine learning can be inputted into AI systems, which provide even more in-depth results. AI systems can then offer specialized guidance for customers and personalize products and services. Banks that use AI can analyze a customer’s activity to suggest a relevant and accurate recommendation.
For example, if a customer receives a large tax return, AI systems can use his or her personalized risk profile to present investment options. An AI system can also recommend travel reward credit cards to a customer that spends hundreds of dollars on flights each month. It’s no wonder 26 percent of respondents to a 2018 survey from Brightedge saw AI as the next big thing in marketing. Soon, applying AI will be necessary to compete in the financial industry.
Future innovation will undoubtedly revolve around providing value for the customer. Adopting machine learning and AI are great first steps for financial institutions—however, developing application programming interfaces (APIs) is a crucial next step.
Using standardized APIs to simplify innovation
The use of APIs opens a world of possibilities for small banks. It allows them to quickly access customer data, gain insights and create innovative outputs. After machine learning and AI predict customer needs, APIs are able to discover solutions to those needs, using multiple vendors to ensure customer needs are satisfied in the most efficient way possible.
The financial industry is working on standardizing APIs to create guidelines for developers to follow. Organizations that integrate this common standard for APIs would use the same interfaces, resulting in streamlined operations, reduced costs and easier innovation for financial institutions of all sizes.
Technologies like machine learning, AI and APIs are driving innovation. Financial institutions now have the ability to personalize services for their customers, which will drive customer satisfaction and loyalty. In a time when personalized services are becoming the norm, it’s necessary for institutions to use new technology to innovate or risk displacement.
Suresh Ramamurthi is chairman and CTO of CBW Bank in Weir, Kansas.
