Machine Learning Platforms Help Banks Meet Customer Demands for Digital Solutions
Faced with increased consumer demand for digital payment solutions, retail banks are working tirelessly to adapt existing business models to attract and keep the twenty-first century customer.
One common response has been to tack on individual solutions to traditional services in order to meet customers’ immediate digital banking demands. These patchwork solutions—current system alterations adapted to respond to digital payment demand—can seem like the right way to go when speed, usability, and security entice banking customers more than traditional incentive programs.
However, these patchwork solutions, lacking a unified architecture and an omnidata view, have knowledge gaps. They expose banks to regulatory risk and fraud attacks. To avoid potentially harmful patchwork fixes, smart retail bank leaders turn to innovative enterprise machine learning platforms to prevent fraud and money laundering and improve the customer experience.
Defining the Digital Banking Challenge
Banks around the globe are having a hard time keeping up with ever-changing regulations and with the rapid evolutions of the digital payments economy, from omnichannel commerce to open banking to real-time payments. As technologies rapidly evolve, consumers are quick to abandon traditional banking models, flock to innovators who have “figured out” customer experience, and take more control of how they handle their finances and their transactions.
Customers want more, faster. But they’re unwilling to sacrifice much in return for increased security. This leaves traditional banks solving for two goals that seem opposed to each other: improving the customer experience while reducing risk.
Legacy Systems Are Problematic in a Digital Age
In 2018, the then CEO of TSB, Paul Pester, was forced out after a migration issue from a legacy system caused a complete IT meltdown and subsequent PR fallout. While some banks may view this as a cautionary tale, in actuality it speaks to the problems legacy systems present in the digital age. Banks who choose to hold on to their legacy systems too long can face severe future consequences.
And major banks have operated under the “if it isn’t broken, don’t fix it” business model for decades. As a result, the systems supporting many of these institutions are just as old. It’s these legacy system roadblocks that prevent easy adaptation and implementation of modern digital innovations, and it’s why so many banks continue to struggle.
In addition, banks used to know their customers through the personal relationships they built when their customers visited branches. However, as digital transformation marches onwards, banks have to rethink how they build the same profile, and maintain the same level of relationship. This is where AI capabilities become increasingly important. AI enables banks to gain a level of understanding of their customers equivalent to that which they used to have when they met their customer in person, but all while transacting with their customer digitally.
Current Banking Solutions Are Failing
Retail banks have realized that relevance in a digital age doesn’t come easy. New, exciting financial technology (fintech) aims to cut out the bank middle man, and it’s leading many institutions to adopt digital systems and platforms that still prove vulnerable to fraud. When security is top-of-mind for consumers, vulnerability is unacceptable.
To further complicate the issue, open banking initiatives like the Payment Services Directive (PSD2) add another layer of necessary compliance and security foresight. Retail banks have to account for consumer demand, customer satisfaction, security, and all the in-betweens when searching for the right digital banking solutions.
Putting Machine Learning to Work
Unfortunately, the more diverse the digital offering, the more attack avenues fraudsters have to access data and cause damage. The answer is machine learning.
AI-enabled fraud prevention platforms that are flexible and scalable can bridge the gap between consumer demand for digital solutions and regulatory compliance, while also improving customer experience. Stopping fraud in this economy ends up being about so much more than stopping fraud. A state-of-the-art fraud platform means a bank can open a new channel without incurring new fraud, and seek out new innovations on customer experience without being vulnerable to new attack vectors.
Machine learning platforms that are purpose-built for fraud combine AI technology with risk management tools and science that come out of years of experience and iteration in fighting financial crime. As a result, machine learning improves customer experience by organizing and analyzing important data, serving up solutions faster than customers can present challenges, and providing 24-hour ML-driven support.
Unlike unsuccessful modern digital banking solutions, machine learning platforms built for fraud:
- Create solutions that iterate quickly around models
- Score transactions in real time
- Allow fine-tuning of models over time
- Streamline the enrollment process
- Keep customer accounts safe from account takeovers
- Score PSD2-related events and transactions
- Allow analytically driven orchestration and authentication
Forget patchwork fixes to legacy system pitfalls. The right machine learning system lets you remove that slow and costly barrier to change: building new data sets and structures to underpin new initiatives. In a world where the rate of change is moving so fast, the power of AI is about connecting intelligence at the speed of innovation, outpacing fraud, and catching up to the future.
To learn more about how Feedzai supports future-focused banks who are going through digital transformation, download our Ebook here.
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- Machine Learning
- Digital Transformation