Agile & Compliant Banking: How to Achieve Both within an Era of Increasing Regulation

Digitalization has sped up changes in the banking industry, but in the area of compliance, things are still running slow. In an industry wracked by fraud, identity theft, and other financial crimes, banks are being asked to maintain transparency on the masses of data they handle. The compliance burden costs the industry heavily in both time and money. The price of non-compliance is too big to ignore - $11 billion in fines worldwide for KYC and regulatory non-compliance as of 2021 - but so is the threat of the agile fintechs snapping at the heels of traditional banks. 

Fintechs or Neo-banks already have the hearts (and wallets) of the up-and-coming “instant gratification” generation. Up to 80% of Gen Z are already using mobile banking and have come to expect near-instant delivery of products and services. It’s clear that to thrive in the coming years, banks will need to be both agile and compliant. Think it’s impossible? It’s not. 

Digitalization of the banking industry began in the early 1990s with a large number of banks dipping their toes in the water. Since the onset of the Covid pandemic, many more have joined their ranks. However, the pace of change means that earlier adopters of technology may now need to revisit and upgrade. It's acknowledged by most in the industry that digital solutions will be essential if banks are to be able to provide online and personalized banking offerings at the speed consumers demand.  

Where there is less awareness is in the area of technology for governance and compliance. As regulations become more complex, the inability to satisfy compliance demands fast enough will hold banks back. This is where technology will come to the rescue yet again. In fact, the very same technology that is helping banks keep pace with the new fintechs on the product side also holds the key to remaining compliant in an increasingly demanding and punitive regulatory atmosphere.   

Data can be a complement and a concern at the same time. 

In the new digital economy, data is gold. A solution that enables banks to efficiently gather, process and derive insights from real-time data, forms the basis of personalized banking products that are helping banks meet customer demand and win in this market. At the same time, data can be a cause of concern, if it's unorganized and pouring in at pace and scale. Digital solutions only work as they should if the data that feeds them is clean and well-organized but, with data pouring in from multiple sources every second of every day, “data overload” is becoming a real threat - and governing and monitoring all that data is becoming equally unmanageable.  

Failure to correctly maintain data damages a bank’s reputation and can lead to hefty fines. It’s no wonder that data scientists spend around 60% of their valuable time on data clean-up and organization - time that could be better spent on higher-level tasks.  

To stay compliant, banks must keep records of all data sets used, any changes to the data sets, as well as who worked on the data sets and when. Many traditional banks use legacy systems, and pricing and modeling are often still done manually using spreadsheets. Keeping records of manual processes is a challenge in itself, and the risk for error is much greater when data is manually passed from department to department.  

At every stage in the life cycle of a banking product, a lengthy and complex due diligence process must take place if banks are to be able to demonstrate that the data used was clean, accurate, and correct. Failure to do so properly will leave banks vulnerable to non-compliance scrutiny and even litigation.  

Data Vulnerability Throughout the Banking Product Life cycle  

Stage in product life cycle	Data due diligence required
Data Management	Which dataset was used to create each product?  Was the data changed at any point in the life cycle of the product?   If so, have you tracked this properly and maintained full records?
Modeling	Which models were used?  Which data was used to train the models?  Who handled the data?  Have you retained the records?
Pricing	Which models were used to create prices?  If the data was updated or changed, can you trace this?  Who worked on the prices?  Have you retained the records?
Testing	Which tests were run?  Who ran the tests?  Have you kept full records?   Are you sure there were no errors when passing data between departments?
Deployment and fine-tuning	How many departments were involved in the deployment and fine-tuning?  Was data manually uploaded or automated?  Who handled the data?  Have you archived the records of every transaction?
Monitoring	Is the data being monitored for all possible scenarios (i.e., different rules in different states)?

What Happens When Data & Models Aren’t Governed Properly? 

When failure happens at any stage in the product lifecycle, the consequences can be severe and far-reaching. This is because errors don't just impact the data itself but also affect the models which rely on the data to create pricing and products.  

In an EY survey on 21 European banks, it was discovered that 

52% of those banks consider it a high priority to move to a data-led approach for compliance functions. 

In addition, they also ranked technology adoption as the top-ranked compliance function. 

As the banking industry evolves, analytics-driven banking products are becoming increasingly popular. To offer banking personalization on products based on data, models using machine learning and AI are needed. Models that rely on accurate and reliable data. 

For the purposes of compliance, it's essential to demonstrate which model and which dataset was used - yet this is getting harder and harder for a variety of reasons: 

Multiple tools - Banks use many different tools and platforms for different purposes. If they are not vigilant, it's easy to be inconsistent with the data source used to build each model.  

Too many cooks - Banks, especially large ones, have teams of data scientists. Data is passed between individuals and teams all the time. If each team is using a different dataset or model, consistency is lost.  

Dynamic circumstances - As market conditions and other factors change, data and the models that are based on them can quickly become outdated, especially if the data is not updated in real time.  

Transfer of responsibility - When employees leave, their knowledge tends to go with them – especially if there is no system in place that monitors and tracks specifically how data is used.  

 

Bottom line, with the huge amounts of data coming into a bank each day from multiple sources at a near-constant frequency, the challenge lies in making sure that every model that is built pulls data from the same source each time it's run. Without this consistency, the accuracy of the insights coming from these models will be questionable - and the same goes for the bank’s compliance record. 

Regaining Control of Your data 

The best way to ensure compliance is to have full control over the data, resulting in full transparency and auditability across all data and models. Proper data governance requires establishing one source of truth for all the data. This will eliminate confusion, paralysis, and bad decision-making.  

Once it’s clear where the data is coming from, it’s then possible to continuously retrain models using fresh data (from that same source) which is always being updated in real-time. This real-time data makes it possible to monitor and simulate new scenarios as market changes happen - with no time lag. The insights garnered are then much more relevant and actionable, increasing the speed at which new products can be brought to market.   

Handoffs are one of the biggest potential minefields. An all-too-common scenario occurs when companies develop a model in one system, test in another, optimize in a third, and pass it through multiple teams before sending it to IT for coding. By minimizing the number of handoffs to other personnel and/or systems throughout the modeling process, the risk of error is significantly reduced. The more automated and streamlined the process can be, the better and safer it is.  

Automate your Banking Compliance

It’s predicted that by 2030, traditional financial institutions will potentially save up to $31 billion of their underwriting and collection system costs through AI technology implementations. 

Not only that, but introducing automation at every step of the product lifecycle will also provide the control needed for a failsafe governance and compliance process: 

• Fewer human errors will lead to reduced costs 
• Reduction in manual processes and handoffs between programs and people will allow for fast operations while still maintaining oversight 
• Staff time will be freed up for monitoring and business analysis instead of fixing data errors 
• Faster speed to market means keeping up with and surpassing the competition
• Full transparency is available to easily prove compliance with all regulations 

The Future of Compliant Banking

The banking industry is evolving fast, driven by the need for data and analytics so as to be able to provide the products and services that customers demand. At the same, the volume of data that banks handle makes compliance trickier and more cumbersome than ever. The best way to remain both agile and banking compliant is to use automation throughout the product lifecycle.  

A system like Earnix allows banks to price effectively while not having to worry about data management challenges, as Earnix (bank pricing software) allows for easy traceability, while ensuring a complete audit trail. Just as technology has already opened doors to new products and services in the banking industry, technology is set to transform the way data is handled and help banks stay compliant under increasingly high-pressure conditions.  

 

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