Nordea

Banks have no choice but to comply, risking being frozen out of global markets. Yet many financial institutions are struggling to scale conformity with constantly evolving global reporting and compliance laws.

Nordea, a globally oriented multinational bank with strong Nordic roots, understands that you cannot apply a linear solution to an exponential problem. Classical financial solutions of people and paper can no longer keep pace. Instead, Nordea applies cognitive automation to keep pace with the increasing regulations and consumer expectations. Cognitive automation is the application of artificial intelligence and machine learning to automate repetitive tasks that require increasing cognition beyond RPA (Robotic Process Automation), relying on high human involvement.

The heaviest burden in operating a compliance process, where bank employees check every reportable customer, is to confirm the reporting classification is correct—to the tune of many thousands of customers each day.

These bank employees also conduct spot-check on non-reportable customers for due diligence. These spot-checks result in several hundred customers out of millions and millions that need to be efficiently filtered. The equivalent to finding a needle in a haystack.

Operating manual processes at such scale is neither efficient, strategic, nor faultless.

Human error increases the probability for false positives and false negatives. Compound this human error with increasing customer expectations and increasing requirements on privacy, regulation, tax reporting, and financial crimes and it is hard to expect banks to keep track of, and maintain high accuracy against, all of these new requirements.

The reputational and operational risk for improperly classifying a customer to the IRS is significant.

A customer who is audited at the fault of their bank’s own human error is a costly consequence; leading to eroding customer trust, closure of accounts, and losses in total assets for the bank. Alternatively, the bank must identify potential fraud for reportable customers that are deliberately not reporting.

Nordea realizes its customer data, over 10TB, is an asset towards automating foreign tax reporting processes that accelerate and improve accuracy, minimize non-strategic personnel efforts, and maximize customer satisfaction related to financial regulations and compliance management.

Therefore, placing rules into analytical models in the form of automation is an easier approach.

This means that no data is left behind or considered garbage prior to ingestion and reading. 

The benefits are that the more data that may be ingested, the more accurate predictions and answers may be, as well as only discarding data when deemed invaluable.

Nordea uses native Vantage filtering and sorting techniques to remove the superfluous data and only make the valued data available for machine learning analysis. Nordea data scientists continue to do analysis, visualization, and operationalization all in one platform.