Stop wasting time and resources now. This key real-world use case for Machine Learning and AI in banking optimizes customer experience, cross-sells and improves profitability.
If I’ve realized anything over the years it’s that virtually all bankers are also bank customers. To that end, in hopes of creating an “aha moment” from one finance industry insider to another, I’d like to put my bank customer hat on for a second. Below, I’ll explore how machine learning and AI in banking — behind the scenes can create experiences that benefit both bankers and clients.
Bear with me as I describe a recent experience I had with my bank which, for the record, is a Tier 1 that I have dealt with my entire adult life and have reasonable affinity for despite this experience.
To set the scene and, for what it’s worth, this is the only institution that I bank with. My footprint, therefore, is quite wide. Here’s the high-level breakdown:
- Primary checking account
- Primary savings account
- Credit Card
- Investment savings account(s) — 401k / Portfolio / RRSP / TFSA depending on where you live
A fairly standard mix.
Missing one key product
Last month I was shopping for a vehicle. You may have noticed that “auto loan” is missing from that breakdown. Now, I’m sure auto loans are a messy product to manage on a good day, but I was shocked about how slow my institution was able to move despite my footprint. While acknowledging that, from a banker’s perspective, auto loans are a complex beast that many opt not to dabble in — remember I have my customer hat on today.
So, I found that perfect vehicle, negotiated the deal, put it on hold and was presented with a variety of reasonable ready-to-sign financing options from the dealer (outsourced to two other Tier 1 banks in my country). Being the loyalist that I am, I pressed pause and said, “I’m going to give my bank the opportunity to pitch for this business.” After all, I do all my banking there and they have all the data to come at me with their best rate. All things being equal, I would choose to go with them.
I can hear you laughing through the screen at my naivety
I quickly realized that virtually zero of the above footprint had any influence on my banks ability to offer me a loan. What may sound ridiculous to you as a banker, is a totally reasonable expectation from a customer point of view. Was I that crazy to think that my bank could offer me an auto loan? As it turns out, yes.
It started with an email to an advisor at my branch who I deal with a few times a month. I let them in on the news… found a car. Ready to buy. Here’s how much I need to borrow and the rates I’ve been offered on-the-spot. Can you match or better? I’ve banked with you for years and I’d like to you to have my business.
The response essentially summed up to this:
- That’s going to take us at least a few days, maybe a week.
- We have to send the paperwork to head office to get a rate.
- Our rate is probably going to come in several points higher than this offer.
- You should probably just go with the other banks.
- I know this is ridiculous, I’m sorry.
When your bank tells you to go with the competition, it makes you stop and think. I had two companies willing to hand over thousands of dollars to a perfect stranger in as little as five minutes, but my bank of twenty years couldn’t even put together an offer let alone compete. With your customer hat on — you tell me who’s crazy.
AI in banking – The future is now
Now, imagine this for a second. What if my bank was able to — through AI in banking + Machine Learning — predict my need for a vehicle in advance? Then imagine if they could have the product (in this case a loan) or a bundle of products surfaced to me via my branch manager or even better, self-service on mobile. A full offer ready to go, ready to execute right when I need it, or before I even know I need it.
See, with a solution involving predictive analytics, the bank could potentially have:
- Identified my need for a new vehicle through an analysis of my spending patterns e.g. increased trips to a mechanic, increased spend on transit, fuel patterns and a daily commute etc.
- In the background have analyzed and compiled a set of potential products or instruments that might appeal to my situation.
- Created a micro-segmented pricing strategy based on any of the factors in my footprint including: region, ratios, balances, etc.
- Presented this to me proactively (before I went shopping) or have it ready on the shelf for when I inevitably reached out.
Vision is reality
This functionality may sound visionary, or ambitious. I understand that this is the exact direction that banks are heading, and have been for some time. When I speak with industry analysts, this use case comes up quite often as something their partners are working towards. Due to legacy core banking systems , as we wrote about earlier, many banks are struggling. Rising IT costs and backlogs make the ability to move at the pace of the market a challenge. That’s where Zafin and the FinTech industry are helping to accelerate innovation for cloud and digital banking.
I reached out to our Chief Analytics Officer, Suman Singh to add his thoughts on the scenario. As a result he ended up affirming this could be used by virtually any bank starting today:
“Yes. Right now, this could have played out exactly as described. A function within Zafin Analytics called Predictive Score helps a bank to mine their core transaction data, spend pattern, transaction activities, payment pattern, payment preferences, etc. Then predict the next best logical product using advanced machine learning algorithms. Another function, which we call Offer Curation helps a bank to identify customers who are highly likely to buy a bundle of products and services. A bank could, for example, select a bundle of three products credit card, personal loan and bill-pay and execute this bundle in our offer curation platform to surface customers who are highly likely to buy.”
A real-world use case for AI in banking
There you have it — vision is actually reality. It is possible today. It can be implemented within the restraints of your existing core system or IT framework with relative ease. You can provide this experience to your clients which could enhance loyalty. More importantly for you with your banker hat — this creates efficiencies down the entire line. You’d be crazy not to.
If I’ve piqued your interest, request a demo here to learn more.
Zafin (@zafin) is a leading financial technology provider that enables banks to form richer, more personalized client relationships. Built from the ground up for financial services, its platform empowers banks to enhance revenue and operational efficiency. Founded in 2002, Zafin sits among North America’s top FinTech companies, and is trusted by retail and corporate units at some of the largest banks worldwide. Headquartered in Toronto with global offices, Zafin has a proven track record with a 100 percent client retention rate as validation.
Don is the Senior Director, Growth Marketing & Communications at Zafin. As former editor in chief and contributor to numerous publications, Don is passionate about sharing stories. At Zafin he will drive a clear and compelling narrative focused on digital banking and innovation in FinTech. Follow him on Twitter @donnyhalliwell
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