Analytics – Banking’s New Best Friend

analytics data bank friend use

We are a month and a half in, and COVID-19 keeps hammering in some fundamental truths about banking operations. It’s now clearer than ever, that the establishment of a fully analytical, highly digitized and automated organization is the logical next step for banks. We’ve known for a while that manual processes are outdated – but how to approach an organizational analytic overhaul exactly – and what will be the immediate benefits?

 

No such thing as normal

Normal was and is a luxury when it comes to credit and pricing decisions. A rigid organization is a toothless tiger, ridden of its main strength – it is merely dragging itself after the market but never catching up. Hoping for steady circumstances, so it won’t have to change (be changed) regularly.

A “normal” environment is rare. You don’t need a worldwide pandemic to rush into use cases for an agile credit and pricing decision system. Less significant volatilities happen every day on the market – smaller events tip over funds available and prices all the time. Banks have been in dire need of a highly adaptable system for a while now.

COVID-19 only amplified the message in a time when keeping financial institutions back books (existing customers) stable and intact became an unquestionable priority.

 

Customer retention is critical

In a crisis, your institution’s relationship with its customers becomes a game-changer.

Clients who are now relying on their banks to help them navigate a financially sensitive period will either feel like the institution was there for them or will be inclined to look for another provider.  Institutional decisions made during a time of crisis cast a long shadow – reaching well beyond the time of the actual emergency.

But how to be there for a customer, in the right way, at the right time? With a well-structured, thoughtfully built data and analytics-driven, customer-centric organization backed by an end to end system that supports operationalization and automation of analytics.

 

Variations on a crisis 

Only in the US, unemployment numbers shot up to 14% during this past week. Large chunks of the population have been affected, the life of many has been put on a dramatically different track from where it was just a month ago.

Banks have decisions to make: will they be there for these customers now, leaning into this brand-new role, or will they bet on the institution’s health first? Many are reaching out to customers with different solutions– offering, for example, mortgage holidays and more, softening the blow for customers.

Meanwhile, governments have stepped in with bailout packages – handing over the task of distributing and managing cheap loans to financial institutions. How to manage all this? How to do the right thing – while also ensuring the organization’s financial health?

And what will happen 3 or 6 months from now, when customers will have to start to pay again. Many of them will not be able to do so.

Will banks give further leeway to all customers – an additional month or two – or should each customer receive a customized offer? An offer based on deeper and wider analysis of their previous financial behavior, history with the bank, the products they’ve been using, their current employment status – data that’s at arm’s reach for the bank and can enable them to decide about individual customers.

 

The five pillars 

Personalization is everywhere – if a platform doesn’t call us by the first name, we notice. Banking shouldn’t be any different. The 21st century calls banks to progress – while a new generation of fintech challengers pushes them ahead.  The cause of worry, in many cases, is that a fully personalized, analytically agile organization seems to be a farfetched idea when it needs to be built on a brick and mortar base.

But not so fast – personalization does not happen overnight. It is a journey, a process of building a system that creates a 360–degree view of every customer. A system that can differentiate between a family of four where two adults have a full income – and a family of four, where one or both of the parents have lost their jobs. A system that’s capable of understanding that those people have lost their jobs recently.  And while two weeks ago they could have handled the burden of the mortgage payment – they are certainly unable to do so now and need a restructured offer before they default. We have established five pillars that need to be built up gradually to achieve an analytics and automation-based system:

  • Systemization & Integration of analytics – Strong model management & governance and process automation backed by an adaptive technology-based solution
  • Creating an organizational data hub that breaks silos and creates a customer-centric single source of “truth” of the data.
  • Flexible modeling approach – focusing on key questions and tailoring the appropriate model to the appropriate problem by combining traditional models along with more advanced ML models
  • Analytical agility – breaking problems into smaller problems, leveraging experimentation to continuously test and learn, especially in times of uncertainty and new realities
  • Analytical expertise – with a balanced centralized analytical team which is well connected to the business organization and continuous investment in analytical education across the organization

 

The journey of a thousand miles begins with one step – goes the famous Chinese proverb.  Similarly, systemization and the creation of an analytic operation might seem like an overwhelming task, but it begins with small changes. It is these small steps that in the end turn into an end-to-end analytic operation and set the ground for a system that translates strategies and financial objectives into accurate, individually tailored offers.

The cherry on top? Such a system would be able to reach out proactively to the customer, offer help, soothe worries – and turn the provider into the 21st-century financial partner everybody needs right now.

 

Dr. Reuven Shnaps

As the company’s Chief Analytics Officer, Reuven Shnaps leads the Earnix Analytics organization. In his role Reuven oversees Earnix product roadmap management and directs the company’s analytics strategy, as well as research and delivery for Earnix customers worldwide. Reuven has spent more than 20 years developing and analyzing advanced statistical and economic models. Over his 10+ years at Earnix, he has gained extensive experience in crafting analytical solutions, and managing and implementing pricing & analytical projects for leading financial institutions around the globe. Prior to joining Earnix, Reuven worked in the economic and statistical consulting group at Deloitte & Touche in the United States, serving corporate clients across multiple industries. He holds a PhD and a master of arts in economics from The University of Pennsylvania and a master of arts in business economics and a bachelor of arts in economics and statistics from Bar Ilan University.