How financial enterprises are leveraging data science today

Today, every financial intuition is a tech company.

Walk into any conference, keynote or boardroom in the industry, and you’ll probably hear a lot of tech-industry buzzwords like AI, big data, and machine learning.

Most in the industry understand the disruption financial services face in the wake of these technologies, and the pressure to adopt them is massive. Not only can they reduce costs, but they can also significantly enhance the customer experience. In other words, the companies that get this right will have a substantial competitive advantage.

So the question isn’t whether or not they should have a big data or AI or machine learning strategy, because if they’re still asking, they’re already too far behind. Instead, financial enterprises need to ask themselves how they get to market with these solutions faster and more effectively than their competition, and what key areas to focus on.

The answer to the first question is simple; most of the capabilities that are disrupting the financial service industry today are readily available, and organizations can avoid spending millions and waiting months by utilizing an out of the box solution. The answer to the second question is a bit more complicated, as there are three key capabilities financial institutions should prioritize; digitization, automation and prediction.

 Digitization

To this day a shocking amount of financial transactions are still taking place on ink and paper, documents are still being faxed or scanned and checks are still being written by hand. This massive trove of analogue records, however, is impossible to search by keyword, organize in a digital file or extract insights from, at least in its native form.

Therefore, one of the most practical use-cases for machine learning algorithms are digitization capabilities. In fact, there are already a range of ready-to-use solutions available. 

For example, one widely used technology called Optical Character Recognition, or OCR, is capable of extracting text from images, whether they are pictures of checks or digital scans of paper documents. That means no more army of interns spending hours manually entering paper-based documents and data into a computer database, nor any of the privacy concerns or human error issues that result.

Similarly, financial institutions are employing voice recognition software in conjunction with named-entity recognition (NER) to turn phone conversations into digital transcripts, and autonomously extract data. As a result, customer service representatives don’t need to spend hours manually entering information into a database, as key data is autonomously extracted and organized for them. 

While organizations once spent millions of dollars and months of manpower building these capabilities, bank-grade NER and OCR technologies are readily available on the open market today for a fraction of the cost of developing them in-house. 

Automation

Once documents, transcripts and other data sources are transformed from analogue to digital formats they can be stored, sorted, searched and analyzed in new and innovative ways through automation. 

For example, the state of California recently passed the California Consumer Privacy Act, which requires significant regulatory changes to any company that deals in personal data, including financial institutions. The new rules require financial enterprises to strip their data of personally identifiable information (PII) that can be traced back to individual customers, and other states are expected to follow suite. In fact, similar rules have already been put in place as part of Europe’s General Data Protection Regulation (GDPR).

Staying compliant in California or Europe now requires the removal of all PII; that means sifting through every digital data storage container to ensure compliance. Instead, organizations can use an out-of-the-box PII Detection algorithm to run a diagnostic test of all digital storage systems and ensure none are out of compliance.

Prediction

One of data science’s greatest capabilities is the ability to predict the future. Instead of a crystal ball, however, complex algorithms sift through massive data sets to pull out consistencies that enable organizations to follow patterns towards predictable outcomes.

One of the most prominent use-cases for prediction tools in the finance industry is in the area of fraud detection. Fraud and financial crime is a trillion-dollar a year industry, and financial institutions are expected to spend $9.3 billion on online payment fraud detection alone by the year 2022. The primary tool in their defense arsenal are algorithms that sift through years of financial transaction records to detect activities that deviate from typical patterns, and flag them as potentially fraudulent. 

Another key use-case for predictive technologies in financial services is the simplification of regression models. Once a highly manual and time-consuming task that required at least some basic coding skills, analysts and industry professionals no longer need to build their own models from scratch. Out-of-the-box solutions take out all the grunt work by allowing analysts to adjust variables in an easy to use interface to see how they perform, no coding skills required.

As with NER and OCR technologies, these tools were once prohibitively expensive for small and even mid-sized institutions, but now a wide array of highly advanced and secure prediction algorithms are available on the open market.