The First Step in Reaping the Benefits Is Understanding the Differences and Real-World Implementations
Ted Bissell is Global Director of Digital Consulting at Axis Corporate. He has more than 30 years of experience helping financial institutions implement innovation.
In today’s Fintech-driven world, big data is the new currency. As companies infuse more technology innovation into the financial services ecosystem, data analytics has made leaps in accuracy and sophistication — creating new realms of untapped opportunities to serve customers better, increase operational efficiencies and increase product reach.
Traditionally, banks and financial institutions had not given adequate priority to structuring their internal data, which limited opportunities to capitalize on analytics that would have delivered value within their companies. The data has existed for years, however, difficulties in bringing together the right software, services and technology prevented them from making sense of it all.
Times have changed. The industry has evolved, and new technologies have entered the market. This has given banks and financial institutions new techniques to maximize investments in data and realize significant ROI when establishing analytics as a core company value. This influx of technology has also accelerated how customers engage digitally with banks and financial organizations, providing them with more-sophisticated ways to consume financial services.
Through the application of analytics across financial services, businesses are may now use data to form better customer relationships, offer more complex products at greater scale and better compete in the market with offerings precisely targeted to match customer need within context. By intensifying the use of unstructured and live-data sources, the benefits can be realized even quicker.
Types of Data Analytics Used in Financial Services — and Real-World Examples
Within financial services, there are four classic types of analytics. Traditionally, the most popular among these had been descriptive and diagnostic, which focus on historical data — typically supporting human decision processes. The higher levels of analytics, predictive and prescriptive, project future outcomes and therefore lend themselves to spread across financial services. Here, machine learning and other types of artificial intelligence (AI) mean that companies and customers no longer must be in direct communication, allowing transactions to happen instantly—with no time for live human intervention.
Let’s break down the four types of analytics and show how they’re being applied in the real world.
- Descriptive: This type of analytics creates a summary of historical data and is useful for fraud-event tracking and risk assessment. Segmenting customers into specific types and relying on better, descriptive data helps companies deeply analyze their sales cycles. Advantages that exist within this category include the ability to better data mine, understand patterns and the chance to focus on past performance to create better future opportunities.
Real-World Example: Feedzai is a platform used by payment-card issuers to examine commerce transactions and patterns to lower credit card payment risk. The machine-learning engine tackles several data points than previously used in payments. This technology creates behavior signatures for merchants and individuals, allowing a robust risk score to be generated. This score serves an advisory role as an input to an issuer’s broader fraud detection platform, with the decision to approve or deny a transaction left to the card issuer.
- Diagnostic: When determining how to better reach a target audience, diagnostic analytics is most useful in understanding who the customer is. When executing advertising campaigns or improving customer service performance and customer relationship management (CRM) effectiveness, this type of data helps measure ROI and overall impact.
Real-World Example: Kasisto is a chatbot that engages financial services customers in conversation, permitting a better understanding of their needs. It targets suggestions for existing financial service product offerings and spots areas of interest to guide the structuring of future offerings.
- Predictive: With this type of analytics, the emphasis shifts toward decision-making and forecasting the future. Particularly for product strategy, marketing resource allocation and enhancing onboarding efficiencies, predictive analytics can automate decisions, and use patterns of past habits to predict future behavior. Outcomes here can help automate customer interactions, keeping up with customers who are already arriving on the scene with their own agents and bots.
Real-World Example: Hearsay, a software-as-a-service digital marketing platform, concentrates on synthesizing insights about customer engagement on social media channels — incubating the process for automating marketing tasks for financial services. Through the platform’s analysis, it can suggest which individuals a service provider should interact with based on their previous behavior, and what type of approach will elicit a positive response.
- Prescriptive: A key benefit of data analytics is the ability to automate processes and provide product suggestions for customers. When planning to rely on bots to formulate and answer customer questions, or act as a recommendation engine, prescriptive analytics are useful. This will increasingly play a role as customer bots begin to interact directly with an institution’s bot.
Real-World Example: Betterment is a machine-learning-based investment portfolio selecting platform targeting customers who traditionally would not hire a person or firm to handle this task. By providing investment suggestions for this customer, they are able to connect customers to products that best meet their investment goals by overlaying the many complex rules that play into a particular investment portfolio decision.
Better, Faster Outcomes
Companies have always had data to manage. By integrating the data analytics described here that rely on machine learning, automation and AI, financial services companies may eliminate unnecessary human interaction that can lead to overlooking a customer need. This progression of technology has forced organizational readiness, streamlined customer operations and boosted revenue streams by making it easy for a customer to choose a service earlier in the decision-making process.
As the financial services industry sees traditional human-to-human customer interactions give way to customer bots communicating with the institution’s application programming interface (API), only careful deployment of machine learning and cognitive computing will allow for effective real-time, on-the-spot decision making demanded by the pace of these interactions.
The result, in many cases, is the ability to rely on an AI-based platform, often personified as a customer-service bot, to provide better, faster outcomes that position financial services companies to competitively market themselves. No matter how compelling a human interaction may be in financial services, changes in the surrounding world will build bigger hurdles to climb. Leveraging data analytics, machine learning and AI gives financial institutions the essential tools to break down these barriers.