By Alec Boere and Serge Plata

Innovation can be achieved when we cross-pollinate ideas from one field to another. We have good examples of how some of the best ideas come from outside the industry in question, as stated in a fascinating article in the Harvard Business Review in 2014 – “Bringing in ideas from analogous fields turns out to be a potential source of radical innovation… the greater the distance between the problem and the analogous field, the greater the novelty of the solutions.”

Tree-search algorithms or simply “search” algorithms are commonly used in artificial intelligence and computer-based problem-solving. It is the process of searching for a solution for a given problem by representing it as a search tree (more generally an acyclic graph). For instance, in games like chess, the board positions correspond to nodes (or vertices) and the moves to directed arcs.

The basic tenet of tree-search-algorithm can be implemented to solve problems in business too. But this is not as easy as importing people from other fields to the one you are working in. It requires some steps, due diligence, and methodology…


3-Step Methodology for adopting tree-search-algorithms

  1. Identify what the structure and essence of the problem is 

We need to make an abstraction of the problem and realize that if the problem is too specific then it would be hard to adapt other solutions to it

  1. Pose the problem in such a way that other people from other industries can connect 

We need to speak two languages and concretize important points so people not familiar with the industry can actually interact and meaningfully contribute

  1. Apply research methodologies in conjunction with engineering methodologies

Put together a team of designers, business analysts, research scientists that design solutions, but also a team of engineers that can land those solutions and ideas.

In our specific case, we have taken an approach to radically innovate within the field of commercial and corporate banking based on cases normally used – and sometimes fully developed- in retail. We have applied the above 3-point methodology to the following question.

How could we enable relationship managers (RMs) to use the advances in digital technology to augment the service they currently provide to their customers?


 Leveraging the Retail Experience


Well, we know that customer relationship management (CRM) has been a common practice in retail for around ten plus years. This activity normally sits in the marketing teams, which are in charge of SEO, email campaigns, and publicity among others. These practices while not new to the banking industry within a marketing sense -especially within a consumer-focused retail banking segment- haven’t been applied to that of a traditionally high-touch human environment for bankers in commercial and corporate banking.

On this basis, we have designed around 20 use cases that we transferred from retail experience into what RMs in banks can do. In addition, we have put together a long-term CRM plan which includes customer vectors, and customer journeys. The technology behind this CRM program lies mainly in:

a) Attribution models that are widely used to optimize communications in retail. Their application in commercial banking will help to ensure these communications (either in person or digital) are hitting the mark.

b) Propensity scoring models, also widely used in retail, that can give us early indications of whether a customer will leave (churn) or engage in services with other providers. It will help us address various questions in customer retention such as- will client X have a high propensity to require additional facilities to support their growth?

It is important to consider these elements with other sources of intelligence. Social dynamics (social physics) is a key element in helping design customer-nudging strategies, to influence healthy behaviors, and most importantly, enable the inclusion of an algorithm that will be able to identify customers’ social identities and in turn help deepen client relationships. This is a novel approach in the banking industry and may even be considered cutting-edge in retail.

Classic marketing mix modeling (MMM) models – once again widely used in retail – can be extrapolated to personalize services and with advanced engineering techniques help RMs to optimize their actions to engage and reach their customer portfolio.


Diagram 1: Complexity simplified and managed for better decision making for relationship managers


Limitless Possibilities for Innovation


When it comes to the cross-pollination of ideas, and exploring common themes for innovation between retail and banking, we have merely scratched the surface. There are so many other areas that we can extrapolate from retail to commercial and corporate banking and this is truly just the tip of the iceberg:

  1. Price Elasticity. Understanding the demand curves of products and services in a heavily regulated industry requires a special adaptation of price elasticity calculations, so the operational margins can be optimized through the price-quantity equilibrium.
  2. Recommendation Engines. This is related to the best next actions that RMs can apply to keep customers happy and engaged, namely using data to help co-pilot their relationships.
  3. Customer Sentiment. This is typically used to improve service, but we also use this information to estimate the probability of a customer leaving. Customer attrition is an important problem that we can tackle from many different angles and not only price.
  4. Inventory Management. This is an interesting area to adapt to banking as banks do not have inventories or warehouses as such, but a deeper client understanding comes from understanding your client’s network. In this case the cash flow between their immediate clients and vendors but also those in the next level down.


By importing retail approaches, banks can enhance their understanding of their client’s cash and credit supply-chain operations and consequently manage the impacts or uplifts as represented below.

Diagram 2: Cash flow network analysis


Co-piloting the Client Relationship


The list can go on and we can keep exploring more ideas for example;

  1.  Price optimization
  2. Intelligent cross and up-selling
  3. Personalized marketing
  4. Augmented reality, which can be used to explain complex projects or future ideas.

One of the ways to engage with the main stakeholders is by promoting the idea of “supply chain transparency” to increase the value of their proposition and ours. There is clearly a huge benefit in explaining the entire supply chain to the final customer as highlighted in a Harvard Business Review article in 2019 by Alexis Bateman and Leonardo Bonanni.

All of these approaches help provide deeper intelligence to the client relationship, what we call co-piloting.

Diagram 3: Co-piloting


We believe in using technology where it makes sense to drive client insights and value, but most importantly moving into a strong advisory space for bankers and their clients. To learn more, get in touch with our experts at Infosys Consulting.

Alec Boere

Alec Boere

Associate Partner, Infosys Consulting

Alec is a seasoned digital and AI expert with over 16 years of experience within the digital space, working with leading agencies and top global brands. He advises firms on the strategic development and delivery of revenue generating or category differentiating digital products and services. He has worked across the digital ecosystem, including, platform builds, apps, social as well as proposition development. His areas of expertise include AI, innovation management, product management, delivery (certified Scrum master), digital platforms, mobile, customer experience and strategy. 

Serge Plata

Serge Plata

Senior Principal

Dr. Plata completed his Ph.D. in mathematics at Imperial College London and has an EMBA from Harvard Business School. He is a certified six-sigma black belt and was awarded a fellowship at the Institute of Mathematics. In addition, he is a chartered scientist in the UK and has published papers and books including “Visions of Applied Mathematics” published by Peter Lang Publishing Co. This year, he led a group of scientists on groundbreaking research on Covid-19 under the initiative from the Royal Society of London (equivalent to the academy of science in any other country).  He has a wealth of experience building, leading, and managing technical teams at a senior level in retail, and consulting from operations to R&D.

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