Today’s banking customers expect real-time interactions and are used to getting an instant response when using applications or accessing services.
Banks and traditional financial services (FinServ) companies must modernize operations to offer new products and services their customers desire. They are being pressured by startups who leverage the latest technologies and are not burdened by legacy systems.
These new entrants have capitalized on modern customers’ expectations stemming from living in an always-on, instant action world. FinTech startups are taking away business from the traditional financial institutions by offering products and services that meet rising customer expectations. Specifically, their applications address the point that customers expect real-time interactions and are used to getting an instant response when using applications or accessing services.
What is real-time in FinServ, and why is it important?
As the term implies, real-time means making decisions and taking actions in the moment. To work, it relies on using sophisticated analytics on real-time datasets. The analytics methods used are increasingly based on artificial intelligence (AI) and machine learning (ML) models. And the data is streaming, often from multiple sources (some of which may come from outside the institution).
A good example of a real-time banking or financial services application is fraud prevention. In the past, different methods were used to primarily detect and understand fraud. A credit card customer sees an unknown charge and calls the bank. The bank analyzes similar incidents, identifies a pattern, and puts rules in place to prevent the same type of incident from happening again.
A real-time approach would look at transactions as they are happening and block transactions from known malicious techniques or agents. Even better would be a system that used AI and ML models to identify unknown attacks that are generating anomalous transactions in the making and prevent them from happening. The use of real-time data and analysis transforms operations from passive (acting after an incident happened and blocking known attack techniques) to active (blocking new and suspicious transactions in real-time).
Beyond fraud, there are many other areas where FinServ companies are looking to real-time to improve operations and grow their businesses. Examples include:
Enhanced customer experience: Today’s marketplace dictates the use of modern applications. Customers are not only used to instant access to their own data and other information but also expect personalized recommendations, immediate response to their queries, and instantaneous decisions on any matter related to their business with the institution. The only way any of this is possible is by using real-time systems and applications.
Intelligent risk and compliance decisions: Regulations impacting banks continue to grow, as do the complexities of managing risk. There are now more robust international anti-money laundering standards, a greater emphasis on anti-bias protections, and expanding data privacy mandates. It is no longer enough to submit a monthly report on activities or conduct an internal evaluation once a quarter. Meeting and demonstrating compliance to governing entities requires continuous vigilance and in-the-moment reporting based on the fast analysis of real-time data.
Enabling new customer-desired front-end apps empowered by back-end systems: The real action in digitalization and modernization is on delivering innovative front-end applications. FinTech companies excel at this. Banks that do something similar while integrating legacy back-end systems have an opportunity to differentiate their offerings. Specifically, FinServs can use real-time data from front- and back-end systems to build applications that provide a multi-channel customer experience. Such applications would allow customers to conduct their business seamlessly while moving from online to app to in-person.
What’s needed?
Businesses in a broad range of industries are all interested in realizing the benefits and the promise of real-time operations. However, there are many challenges. Infrastructures must be in place that can accommodate the volumes of real-time data that need instant analysis. In any business that is not a startup, existing platforms may not be adequate.
FinServs have additional obstacles when trying to transform to real-time. Mini-computer and mainframe legacy systems are the backbone of their operations. So, they must integrate data and applications from those systems. That’s a burden many businesses, whose operations are built on cloud and on-premises servers, do not have.
An additional obstacle is that FinServs operate in a highly regulated environment. They must dedicate resources to ensure uptime, availability, data privacy, and more.
What’s needed is a modern data platform. The platform must be low-latency and remain highly responsive as it scales. Furthermore, it must:
- Allow for rapid application development and deployment
- Support a move to cloud and use of microservices and container technology to build modern applications
- Easily incorporate new technologies (e.g., sophisticated analytics and AI) as they emerge and are needed
- Offer enterprise-class characteristics including reliability, resiliency, security, etc.
Selecting a technology partner
FinServ systems need a new set of capabilities to meet the demands of today’s customers. In particular, they need real-time performance at scale, modern data models, and enterprise-grade security and compliance.
Many companies opt to team with a technology provider to develop such capabilities. What’s needed is a partner that brings deep industry expertise in banking and financial services and offers a robust (enterprise-class) solution based on industry standards.
These are all areas where Redis Labs can help. The company is the home of Redis, a popular in-memory database, and the provider of Redis Enterprise, which delivers the performance and reliability needed in FinServ production environments.
Redis Enterprise delivers the high write throughput and multiple data models needed to keep customer profiles updated in real time for dynamic pricing, credit-risk analysis, targeted advertising, credit-card promotions, and more. These capabilities can help improve customer engagement and personalization.
Redis Enterprise helps FinServs examine patterns across transaction histories, perform geospatial analysis, and check transactions against known fraudulent patterns.
Critical to any real-time application is response time. Today’s customers are accustomed to instant everything in all aspects of their life. If their bank cannot meet that expectation, they will give their business to a FinTech who can. Redis Enterprise addresses this issue by using application caching. Redis Enterprise increases application response times by serving frequently needed data from an in-memory cache instead of making calls to a database.
To learn more, read our Q&A interview with Ashish Sahu, Director of Product Marketing at Redis Labs, on FinServ Legacy Modernization via Hybrid Cloud.
