By James Adie, VP of Sales, EMEA, Ephesoft
As a financial services organisation, signing up a new customer involves a great deal of paperwork. In order to meet anti-money laundering (AML) and know your customer (KYC) regulations, the onboarding procedure generally involves the customer presenting proof of identity, evidence of address and proof of earnings. The range of documents required can include passports, driving licences, utility bills, bank statements and payslips. These documents are usually scanned, stored as images, and linked to a customer record by some kind of reference number.
Aside from the sheer amount of manual labour involved in checking and cross-referencing these electronic documents, a vast amount of valuable data is going to waste. Every document contains information that has the potential to be a major asset if only it were stored in an accessible database. Marketers from all sectors routinely use software to analyse structured data and to draw valuable insights from details such as age, region, income range and spending habits. Financial services companies are collecting all of these details during the customer onboarding process, yet they are stored only within scanned images of individual documents. These “flat” image files, occupying storage space and accessed rarely, are next to useless. As stated by IDC in a recent report, “Data, in the absence of meaning and context, is worthless and costly.”
Extracting the value from an everyday document
To address this, we need technology that can identify and extract the unstructured data from a “flat” image of a document and turn it into something useful. Some financial services businesses are beginning to use data capture technology that uses Artificial Intelligence (AI) in order to process the documents presented by new customers. This type of software can extract the relevant information from a digital image no matter what the format. It learns to identify specific fields on different types of documents, such as the account number on a bank statement or the name and address on a utility bill and can be trained to recognise all of the documentation required for a particular product, whether that’s a mortgage application, a loan or a new bank account.
Having identified a document and the important fields within it, the next step is to add meaning to this information by putting it in context. At its most basic level, for example, the name and address on a utility bill can be cross checked against the name on the application form, but far more is possible. When a document comes in to support a mortgage application, for example, it should be possible to link it not only to a customer’s account details, but also potentially to an individual’s credit record, to the history of the house being purchased, or even the insurance details of the property.
By extracting this information and adding context to it, financial services organisations are transforming a stack of flat images into a three-dimensional treasure trove of accessible data.
Levelling the playing field
AI-based technology is opening up a wealth of new marketing opportunities. For years, the likes of Facebook and Google have been gathering data from their customers and using this to target them more accurately. In retail, we now take it for granted that the brand offering us a new pair of shoes or a tracksuit for our dog will already know that we like brogues and own a Labrador. It’s time for financial services to achieve a similar level of customer understanding. Until we can fully analyse transactional financial data we won’t have access to the huge volumes of information that retailers have at their disposal, but ensuring that a customer’s credit record is assessed in the context of their other accounts is a good start. We certainly need to move beyond the current situation, where a lender will often struggle even to recognise a mortgage applicant as an existing customer.
Using AI-based data extraction technology in the onboarding process goes far further than the practical issues of efficiency and time-saving. It’s true that processing time can be slashed, customer service improved and compliance made more effective, but in the long term, it’s the improved understanding of the customer that will help financial services organisations to grow their businesses. We are seeing a raft of fintech startups including rapidly growing brands such as Revolut and Monzo, coming into the market armed with an understanding of the importance of customer data, so the established banks and insurance companies need to move fast.
Most financial services companies are aware of the challenges that onboarding brings, and many of them are looking to automation as a solution. The good news is that it’s possible to simultaneously solve a problem and create an opportunity. You can cut your administrative costs, improve your customer service and make compliance simpler – and at the same time start building the data structure that will build your business.
