Brands utilising big data are cultivating an ‘insight economy’ where every business move is mapped out with pinpoint accuracy thanks to the internet of things (objects that send and receive data) building a connected world. Businesses are leaping at the chance to embrace ‘cognitive computing’, a process where coding, tools and data are combined to achieve artificial intelligence (AI), reasoning and learning. Now, more than ever, analysts and data scientists in the financial services have the capacity to deliver concise, data-driven predictions based purely on data and performance, and unleashing lucrative returns. But, that’s not happening.
FLAWED FINANCIAL SERVICES
Credit scoring has stagnated since its birth in the late fifties in the Merchant Associations and small Credit Bureaus. Based on the borrower’s financial behavior, such as frequency and reliability of repayments, plus more surprising factors such as a ‘gut feeling’ assessment of the borrower’s personality, likes and reputation, lenders created a score unique to an individual borrower. The system eventually matured into the contemporary factual credit scoring system, however inaccurate data and grinding processes still haunt the scoring.
Compared with the accuracy of the internet of things, which, according to IBM, generated 2.5 billion gigabytes of data in 2012 and is expected to be collected from an astonishing 75 billion internet-ready devices by 2020, the uncertain credit scoring system feels about as reliable as a chocolate teapot.
UNSURMOUNTABLE SLUGGISHNESS AND INACCURACIES
The credit scoring process, while established, is flawed. Lenders consult Experian, CallCredit and Equifax about one borrower, tripling their workload even though the traditional methods of calculating a credit score are employed by all three agencies. Why? Because although their databases overlap, the information recorded differs across financial packages. If a loan application is rejected by one bank, the same information could be used to fuel another rejection, trapping consumers in a cycle of unsuccessful applications.
Unfortunately, the traditional data won’t reveal the applicant’s intent to pay, their affordability, and how likely they are to make repayments on time. Trustworthy applicants that could easily afford the loan repayments are missing out due to the lack of data that lenders rely on. While these customers are slipping through lenders’ fingers, questionable applications are passing checks with flying colours only to burn lenders further down the line.
Borrowers, advisors and lenders are languishing in a lethargic and sluggish routine of inaccurate data and never ending loan and mortgage applications. Their frustration is at fever pitch. The Mortgage Market Review (MMR) has come under particular scrutiny and criticism for this inaccuracy, citing that while the affordability criteria is rigorous the application process now takes in excess of 40 days, with brokers spending significantly more time caught in bureaucracy when mistakes are made.
BIG DATA FOR THE FINANCIAL SERVICES INDUSTRY
Currently, the information collected and analysed by big data systems such as IBM’s Watson is unmeasured by the credit reference agencies. Unsurprisingly, advisors and lenders are looking longingly at such solutions in the hope they’ll be incorporated into the credit score calculation, because such a time saving and contextually aware tool would change the financial service industry beyond recognition.
The emergence of social media has contributed to the data revolution. By simply overlaying social data onto traditional data, lenders are afforded the context otherwise lacking in their decision-making. What’s more, lenders will be able to take on new financing accounts with confidence. The financial services industry are looking for actionable solutions to bring those numbers to life, but the truth is that such technologies are in existence, either made by tech giants or FinTech startups.
Social media data can be incorporated to enrich reliable credit scores by delivering deeper insight into each customer as an individual, instead of a number. Unlike traditional credit scoring, it reveals recorded events and phrases that businesses can analyse to discover what is going on “behind the scenes” financially. It echoes the original methods employed by the Merchant Associations and small Credit Bureaus, where a part of the credit score looks at personality, and what’s actually going on in a borrower’s life. Social data offers that same insight, minus the one-to-one visit, and brings personalisation into the equation. Affordability assessments will become more detailed and bespoke than ever before and still maximising efficiency, thanks to automated data analysis.
Borrowers that invest in a new boiler, for example, or whether they choose to shop at Lidl or Waitrose, and even if they have just been on holiday, got engaged, or had a new baby, will all be revealed by overlaying social data to usual credit checks. Social data reveals these personality snapshots of overwhelmingly significant insights, so businesses can draw their own conclusions about the financial stability and commitment of the borrower.
A BUSINESS THAT CORRECTLY LEVERAGES BIG DATA BOASTS A STAFF OF ENGAGED, SOPHISTICATED THINKERS
Big data fundamentally changes how staff and computer systems interact. The lengthy and frustratingly repetitive process of manual data entry is eliminated, instead absorbed by systems. Advisors are focused on interpreting data, strengthening their ability to diagnose the borrowers reliability. In another instance, employees engaged in overly complex customer service tasks, that perhaps involve natural language questions, are relieved of Q&A sessions (or phone calls, or emails clearing up mistakes) because systems can ascertain the same information by overlaying social data.
Big data gives financial services back the human touch. Overlaying social data and analysing information beyond pockets of niche data tells lenders a picture of who they are, what they do, what they spend money on and their personality traits. But most importantly, it tells them what the borrower can actually afford.
Original article on Dataconomy