Why Legacy Banking Systems Need a Machine Learning Boost

The truth is the right machine learning platform is a game-changer – if you’re prepared for it. And when it comes to legacy banking systems and machine learning, this isn’t an either/or situation. In the world of fraud and financial crime prevention technology, you can have your cake and eat it too.

Here’s everything you need to know.

The best of both worlds: How machine learning augments legacy banking systems

FIs are frequently reluctant to replace their core fraud prevention and detection systems because of costs and the disruption such an undertaking requires. Fortunately, banks don’t have to reinvent the wheel. Instead, they can augment their legacy systems with machine learning technology. Machine learning solutions can enhance existing legacy banking systems, making the rules that core systems use more effective in the fight against fraud. 

Remember, you’re not reinventing the wheel. You’re upgrading the wheel from a concrete slab by adding a rubber tire and using lighter, more reliable materials. Your current wheel will perform much more effectively with these changes in place.

As customers raise the bar for their banks, relying too heavily on legacy systems could impede your bank’s innovation agenda. Here’s why it’s important to consider upgrading your legacy banking system with machine learning. 

Customer banking expectations have shifted

Today’s customers are accustomed to shopping, ordering food deliveries, and transportation from the comfort of their smartphones. They can also transact easily on their smartphones using services like Uber, Airbnb, and Facebook. They expect their banks to provide the same level of gratification.

Banking’s digital transformation started with ATMs and continued with credit cards, debit cards, internet banking, mobile apps, chatbots, and more. Every innovation has made banking more convenient. Today’s customers can access cash, make purchases, instantly check their balances, and transfer funds in a matter of seconds on a 24/7 basis. The global pandemic increased pressure on banks to enhance customer-facing operations and enable more customers to bank digitally.

The digital transformation isn’t over yet. As the financial services sector rolls out new technologies and innovations, consumers’ attitudes on banking have grown increasingly convenience-focused. This is not likely to change anytime soon. 

Security expectations remain steady

Customers have expected banks to keep their money safe and secure for hundreds of years, dating back to when the only way to access their money was to visit a physical bank branch. Even with the expansion of digital banking channels, bank customers’ security expectations have remained steady.

Fraudsters are also eager to take advantage of new digital banking channels to steal money from legitimate bank customers. For every new innovation designed to provide a seamless customer experience, fraudsters quickly swoop in to exploit any flaws they can find. This is where core legacy systems that rely on rules-based models struggle to flag fraudulent transactions. By the time you write new rules for the system to address the fraud patterns that you’re currently seeing, fraudsters will shift their tactics. While your legacy system can be updated with new rules for known fraud, the relentless nature of fraudsters can leave you feeling like you’re stuck on a hamster wheel. 

Warning! Legacy banking system talent shortage ahead

Legacy systems were designed and developed several years ago. The technology used to support them is becoming increasingly obsolete today. This means banks with legacy technology will struggle to maintain their legacy systems if something breaks down.

Legacy systems could also be a turn-off for prospective talent. Many job seekers are drawn to companies that disrupt their respective industries, and the banking sector is no exception. If your FI is still relying too heavily on legacy banking systems, there’s a good chance you might alienate talented individuals who are reluctant to work for an “unsexy” bank that utilizes older technology instead of machine learning. 

Case in point, demand for COBOL programmers in the U.S. surged last year when millions of out-of-work Americans filed for unemployment assistance. Because many state government unemployment systems run on COBOL-based programming, searches for programmers surged by more than 700% in a one-month period. Available talent proved hard to find, however, which left states struggling to handle the surge of new applicants and detect unemployment scammers amongst the masses.

Legacy banking systems are stable, but slow to evolve

Reliability is one of the key reasons that banks stick with legacy systems. By some accounts, these systems process more than £2 trillion per day. While the systems need to be maintained and updated routinely, they are stable investments overall.

What many banks are learning is that stability does not translate into flexibility, however. As the demands of the financial services landscape shift, core legacy banking systems frequently struggle to keep up. These systems’ underlying legacy infrastructure is often rigid, which makes it challenging to launch new products, change features, or stop fraud effectively. Banks that rely on legacy systems will likely find themselves playing catch-up to roll out the latest banking products that customers expect.

Solution: Don’t replace your legacy banking system – enhance it with machine learning models

The banking ecosystem continuously evolves.  New payment types and services emerge and new banking models, like challenger banks and FinTechs gain in popularity. Traditional banks need to upgrade their core banking systems in order to stay competitive and relevant in the eyes of today’s customers.

Modern bank customers put a premium on convenience and want their banks to deliver on these expectations. If customers can’t access the latest and greatest banking tools, have their transactions wrongly declined, struggle to open accounts, or don’t trust the bank to protect them from fraudsters, they’ll switch to another bank.

Fortunately, if you are just starting down the artificial intelligence (AI) path, you will not have to replace your core legacy system entirely. Instead, your bank can enhance the effectiveness of your existing rules with AI and machine learning by strategically placing them in the right step of transaction flow.  

Tips to maximize machine learning effectiveness

You might be hesitant to invest in machine learning because it’s a new technology and you’re unsure if it stands up to the hype. How will you know if you’re making the right call? The best way to ensure you’re investing in the right solution is to adopt the right attitude on machine learning.

Here are a few tips to ensure you arrive at the machine learning promised land, not a shiny new toy detour.  

Tip 1. Know your data

Your data is your bank’s most important asset. Make sure you understand the data that your machine learning models will rely on to detect fraud and protect your customers. Clean and review your transactional and your historical data and make sure fraudulent activities have been flagged correctly. Set up processes and systems that enable staff to label fraud easily and effectively, reducing the risk of human error or omissions.

Tip 2. Let your data guide your decisions 

Proper labeling enables machine learning models to be more effective at detecting fraud than your existing legacy system’s rules. AI can review hundreds of data points simultaneously, whereas rules-based systems can only review four to five at a time. These solutions can analyze fraud patterns you’ve seen in the past and identify fraudulent activities that your legacy system missed. You’ll want to use these insights to improve your bank’s decision-making efforts to enhance fraud detection.

Tip 3. Build a strong team

Machine learning models enhance your legacy system’s rules. You’ll want to make sure that your models are built quickly and rapidly deployed into production. If you build a model and it takes one year to deploy, the effort is mostly wasted. Assemble a strong data science team that can build models quickly and establish processes that move those models into production to maximize your machine learning system.

Tip 4. Make sure it’s future-proof

Legacy systems rely on rules to catch the fraud that you know about. But fraudsters won’t give up when you fix the openings they exploited. Machine learning algorithms can live on top of your legacy system’s existing rules by reviewing contextual information and highlighting patterns around what’s happening with your customers, branches, and businesses. This contextual information can help you prepare for emerging fraud patterns instead of simply reacting to fraud once it happens.

Machine learning might seem like a risky venture. But relying solely on core legacy systems to address today’s banking challenges is a more serious risk. Your bank customers appreciate the benefits of banking’s modernization. Investing in technology that can deliver on those benefits can go a long way in meeting their high expectations.

Artificial intelligence (AI) and machine learning are no longer reserved for larger banks. Download our eBook, Democratizing Machine Learning for Community Banks, to learn the important questions to ask when investing in a machine learning platform.