Sponsored: How AI can grow relationship businesses in banking

DataRobot chief success officer Greg Michaelson assesses the crucial role of artificial intelligence and how banks can best harness the benefits of this innovation 

Making predictions has always been a part of the banking industry, so it’s no surprise that banks were some of the earliest adopters of machine learning for predictive analytics. Today, you would be hard pressed to identify a line of business or function in a bank that doesn’t have multiple needs for predictive analytics, and while all banks realize they must find new ways to learn from data, many banks aren’t harnessing the full power of artificial intelligence (AI) and machine learning. 

At least part of this AI and machine learning deficiency can be explained by the capacity of data scientists and availability of data. Traditionally, data science teams have had their hands full building and maintaining the core models of the bank. Add to that the challenges involved with managing, cataloging, and assembling data, along with the obstacles associated with implementing these models into production, and it’s no wonder that progress has been so slow.

To complicate matters, the number of players in the banking sector has increased dramatically in the last several years. A growing number of fintech companies are making an already competitive market even more so, and they’re doing it by means of innovation and AI. 

So where should banks start? When it comes to AI, some of the lowest hanging fruit today are the so-called relationship businesses. These are typically high-touch, high-profit, complex relationship-driven businesses that have dedicated relationship managers and generate a mountain of revenue for banks; e.g., commercial and wholesale banking, wealth management, etc.

While the old fashioned approach is to keep data largely out of the relationship business, the tides are shifting. Businesses, who are impacted by ultra-low interest rates and increased competition, are now open to exploring new strategies to grow and deepen their relationships. The opportunity to do so is huge.

The use cases

All sales teams use CRM tools to track leads, opportunities, and accounts. That means that all sales teams have incredibly valuable data that tracks where they’ve been successful and where they haven’t. This data is the foundation of building an AI solution to grow a business.

Prospecting for the best new customers. In the world of sales, prospect lists abound, driven by inbound leads, cold calls, and third-party data sources. Sorting through it all to know who to contact and who to focus on, can be bewildering — particularly if you don’t have access to any reporting or analysis showing what has been effective (and what hasn’t).

Building an AI solution for prospecting involves first identifying what a “good” prospect looks like. The most obvious definition of a good prospect is anyone that is likely to buy what you’re selling, but it’s far from the only example. Bankers might want to predict the credit quality of prospects based on publicly available data to identify the clients where they want to spend their energy. Or perhaps they might want to predict which clients are likely to produce the most value for their business.  

A growing number of fintech companies are making an already competitive market even more so, and they’re doing it by means of innovation and AI.” 

In any case, once a good customer is defined, then it’s just a matter of matching up external prospect data with a bank’s existing client base to build a training dataset. For example, I might take the data I have available for my prospects (my features) and match them up to my current clients where I know the target value. I might try to predict profitability, product appetite, or margin. Once the models are built, I can then score my list of prospects in order to rank them by potential.

Deepening relationships with your current customers. The number of products and services that a customer needs is strongly correlated with how profitable that customer is. AI can improve the effectiveness of relationship managers by identifying particular customers that are in need of particular products. If a relationship manager can reach out to the right customer with a specific product, then that customer will be happier, sales will be higher, and the relationship will grow stronger.

Fortunately, if the bank utilises a CRM system, the data needed for this project has probably already been captured. It’s simply a matter of identifying which customers were offered which products and whether or not they bought them. Which customer and product attributes will be predictive of purchasing likelihood will depend on the product.

Once I have my models in place, it’s relatively straightforward to identify the best prospects for a marketing campaign or to have an offer in the “back-pocket” for every customer interaction.

Pitfalls to avoid

These are just a couple of examples of ways that AI can increase the effectiveness of sales teams. Building the solutions is not complex from a technical perspective and deploying them is also pretty straightforward. That said, there are a few blockers that you will want to avoid as you build these solutions.

Don’t fail to record information about lost sales. Capturing data about lost deals is just as important as capturing data about won deals. In order to build models to predict customer behavior, all the possible outcomes have to be captured.

Don’t wait until your data is perfect. Everyone has data issues, but starting with the data that’s currently available is the only way to get started with AI. Waiting until everything is perfect means never getting started at all.

Don’t expect it to work perfectly the first time. These solutions, even though straightforward, take some iteration. 

Prospecting and deepening models are two ways most banks can start exploring how AI can impact your organisation.


These use cases are low risk, quick to build, easy to implement, and have a high ROI, and advanced tools like automated machine learning make developing these solutions accessible without the huge upfront cost required in the past.


Whether you’re a large bank or a credit union, with sizeable complex deals or simple term loans, AI can provide a straightforward way to be more targeted in your sales efforts.