By Greg Michaelson, VP, General Manager of Banking, DataRobot
Banks have been using machine learning longer than pretty much anyone else, and for good reason. Identifying the lowest risk borrowers, pricing loans, and forecasting losses all require the use of predictive models. Sometimes these models are simple scorecards. Other times, they are highly complex machine learning models. Many banks have at least some of these core models built, with a process in place for monitoring and maintaining them, but outside the handful of core models, the need for embedding artificial intelligence (AI) and machine learning at a deeper level is an unfulfilled need at most banks.
At least part of this deficiency in AI and machine learning 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. Fintech companies are hard at work gobbling up bits of market share, and they’re doing it by means of innovation and AI. Banks still have the advantage in terms of data and expertise, but that advantage won’t last if bankers don’t start innovating.
When it comes to AI, some of the lowest hanging fruit in banks today are the so-called relationship businesses. These are typically high-touch, high-profit, complex relationships that have dedicated relationship managers and generate a mountain of revenue for banks. Business banking and corporate banking businesses along with wealth management teams are good examples of this type of relationship business.
Several years ago, I was at a sale offsite for a Fortune 100 company. The attendees were relationship managers that were all compensated on sales. The first half of the day was set aside for discussing prospecting strategies. Salesperson after salesperson stood up and told about the newest event that they had hosted that was really “working” for them. Box seats at sporting events, breakfast meetings, cold calling, and many other in-person events were touted as “the way” that was going to produce results.
At the time, I remember being struck by how unscientific it was. I even made a comment about collecting data to see what was actually working. I was quickly reprimanded: “This is a relationship business,” one experienced salesman told me.
I’ve heard that same sentiment many times since then. “This loan is too complex for a pricing model.” “This relationship is too unique to be lumped into a dataset and learned from.” “This account is much too important to be driven by your models.”
Fast forward a few years, and these relationship-driven businesses are beginning to change their tune. 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 that’s far from the only example. Bankers might want to try 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, either by growing in the future or by their needs for a wide variety of products.
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; e.g., risk rating or profitability (my prediction target). 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 for potential sales efforts.
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 with a specific product to the right customer (and not reach out with the wrong products), 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 a simple 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. Salespeople hate data entry (doesn’t everyone?). They especially hate entering data about deals that they’ve lost. Capturing data about lost deals, though, is just as important as capturing data about won deals. Machine learning requires data that the machine can learn from. That means positive and negative examples. 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 it’s pretty rare to come across a data set-up that is so bad that nothing can be done. 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. I worked with a sales team at a large bank to predict which clients were most likely to need a foreign exchange (FX) product to lock in conversion rates for cross-border payments. It took us six iterations to get to a good model, but when we finally did, we increased the conversion rate from 2% to 10%, which meant a $10M+ increase in sales for the organisation.
Prospecting and deepening models are two ways most banks can start exploring how AI can impact a 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 now make developing these solutions accessible without the huge upfront cost required in the past; e.g., massive time investment, hiring and retaining large data science teams, tricky manual deployments, etc. 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.
The Next Evolution in Banking
By Young Pham, Chief Strategy Officer at CI&T
Everything we know about banking is about to change. A new industry around the sharing of financial data is primed to give birth to a host of new consumer services, all thanks to Application Programming Interface (API) technology. Already known for being the safest place for money, there are opportunities for banks to expand that relationship to other aspects of the customer relationship. Banks will no longer simply be just a place to deposit and withdraw your cash, but a one-stop-shop for a range of data-sensitive services.
The passing of GDPR and the Payment Services Directive (PSD2) were the first steps in this process of banks modernising how they handled their customer data. However, incumbent institutions have so far not engaged enthusiastically. Rather, it was only after growing pressure from fintech challengers and government regulation that they were forced to open up and share their data. This should not be treated as a regulatory challenge, but rather a way to grasp the unique opportunities that banks have to reposition themselves as the most trusted resource for their customers.
It is hard to overestimate the breadth of possibilities arising from open banking, should banks choose to take advantage of this evolution. While the public rarely holds bankers in high regard, it still puts a high level of trust in banking institutions. People are more willing to hand over their sensitive data than they would be to almost any other private entity. Furthermore, banks have a unique perspective into their customers’ behaviours, needs and desires. Spending habits, income streams and risk appetites are just a few examples of the data that no other institution can tap in to.
There is certainly appetite to expand offerings. In our recent study of business banking customers, over 68% of respondents indicated that they were open to their financial institution providing digital non-banking services. This includes services such as tax support, managing payroll, or invoicing to help them with their day-to-day businesses.
More banks should consider how open banking can maximise their digital capabilities and create a greater range of services for customers to enjoy. Such offerings could be tailored according to each bank and their particular customer audience. For instance, banks could offer everyday services for most users, such as insurance for individuals or business management tools for business accounts. Alternatively, banks could offer more exclusive and specialised services for high net worth individuals to meet their specific needs, such as art appraisal and investment management.
The idea that a firm can expand its offering into new verticals is hardly new. Many of the world’s largest tech companies, such as Apple and Amazon, already offer diverse products including hardware, software, entertainment and cloud services. They are able to do this thanks to the vast quantities of data they have gathered, which provide invaluable insights into consumer behaviour and demand. Banks are in prime position to follow the example of these top tier tech companies thanks to their monopoly on key financial data.
Disruptors vs incumbents
The business model described above is already being adopted by numerous challenger banks. These firms have led the innovative charge thus far, thanks largely to their agility afforded by their smaller size. Indeed, some fintech banks already provide a range of non-banking services to their customers. Revolut, for instance, offers users several types of travel insurance as well as access to airport lounges as part of its premium service for a monthly subscription.
These offerings are not a sign that the challenger banks are about to topple the large incumbents. Rather, these disruptors have always flagged the gaps in the market that larger institutions have been too slow to fill. It is now up to the established banks to learn from their example.
While challenger banks may have a first-mover advantage for these services, the incumbents have two key advantages: capital and credibility. Firstly, the top banks have enough cash to fund this overhaul of their business models. While the challengers have been able to afford to do so in recent years, they lack the reserves to tide them over during economic downturns such as the current pandemic.
Secondly, even though challenger banks are perceived as more convenient and are less vilified than traditional banks, the public still trusts the latter. Many of these large banks can point to their extended histories and long-term investment success – accolades young challengers simply cannot match. In short, people don’t have to like their bank to trust them with their cash and their data. These two advantages strongly suggest that large banks are better positioned to take advantage of the open banking business model in the long term, despite being slower to adopt and adapt.
All this opportunity is within reach. We already have the technical capabilities for data sharing, and the regulatory framework is not insurmountable. Rather, the key for this evolution of the sector lies in banks’ appetite for risk and willingness to reinvent their business model.
Banks need to take a leap of faith and leave behind the business paradigm to which they’ve become accustomed. They should embrace transparency, run towards regulation and take advantage of opportunities to invest in these areas or collaborate with outside technology firms. Only then will banks be able to make the most of their data assets, creating value for the customer and further strengthening the relationship.
Banks talk a good game, but are bankrupt when it comes to change and innovation
By Erich Gerber, SVP EMEA & APJ, TIBCO Software
You hear all the time about the incredible pace of change in technology and the way that it affects business, but sometimes we kid ourselves about the real speed of that change and the depth of its effects. Retail banking is a perfect example to illustrate the yawning chasm between the illusion and the less attractive reality. In this article, I want to provide a critique of the banking sector and its failure to change fundamentally and to modernise.
Banking is an old sector: the Banca Monte dei Paschi di Siena has its roots in the 15th century and the oldest UK banks go back to the 17th century. We often talk about legacy holding companies back, restricting their speed of operations and hampering their ability to adapt. Well, established banks have legacy in spades.
They also have cultural challenges. The old saying has it that something is “safe as the Bank of England” and that is a standard for security. But today we need banks to be more dynamic and represent something more than being a deposit box for our wealth. Consumers are accustomed to the superb customer experiences in entertainment (Spotify), devices (Apple), retail (Amazon), travel (Uber) and much else. Surveys show that they want their banks to be responsive, easy to use and available across multiple channels. They’d like banks to be secure but also to be advisors, enable flexible movement of assets between accounts, provide useful data analytics, be cloud- and mobile-friendly and offer deals that are specifically targeted at their interests.
At their core, banks now must become digital enterprises but, frankly, it has been slow going. As Deloitte observed: “While many banks are experimenting with digital, most have yet to make consistent, sustained and bold moves toward thorough, technology-enabled transformation.”
We all know that retail banking has changed significantly: you can see that in the proliferation of apps and the fact that, in pre-pandemic times, the morning and evening commute are peak times for transactions as people arrange their finances while sitting in trains, buses and subways. Banking has become a virtual, often mobile business, thanks to new tech-literate consumers pushing banks in that direction. But my fear is that the banks aren’t moving even nearly fast enough and that’s bad for us as consumers and bad for the banks themselves.
Banks are under pressure to change because challengers don’t have the legacy constraints of incumbents and because PSD2 and open banking regulations are having the intended effect of promoting banking as a service, delivering transparency and greater competition.
Attend any business technology conference and banks will talk about their digital transformations and customer experience breakthroughs, but it’s my contention that a lot of this work is more window-dressing than platform building. Or, to put it another way, banks are injecting Botox, rather than undergoing the open-heart surgery that they really need. It’s a case of ‘look: fluffy kittens and shiny baubles’ in the form of apps and websites, but the underlying platforms remain old and creaking and that means that the banking incumbents are hampered.
To be fair, I have lots of sympathy here. They simply can’t move as fast as the challenger banks that have had the luxury of starting their infrastructure from scratch and sooner or later that will come back and bite them. Look, for example, at cloud platforms where only 10 or 20 percent of infrastructure has been migrated despite promises of cloud-first strategies and the banking data centres where monolithic on-prem hardware still reigns.
You feel that slowness of action in your interactions with banks that communicate only via issued statements, letters notifying you of changes to Ts and Cs, and threats when you go into the red. Inertia is nothing new in banking either: we like to think that technology change happens in the blink of an eye but in banking contactless NFC took the best part of 20 years to go mainstream.
This is the dirty secret of banks. They see the need to change but remain shackled. Why are the banks so slow? Historically, because it was hard for competitors to gain banking licences and the capital to really challenge so there was no catalyst or mandate for change. Also, because change is tough and fear of downtime or a security compromise to critical systems is very real. More recently, because internal wars in organisations set roundheads against cavaliers, the risk-averse against the bold, resulting in impasse and frustration.
I said change is tough and that’s why banks need to power through on the basis of Winston Churchill’s wisdom that ‘if you’re going through hell, keep going.” How? By a combination of maniacal focus on expunging legacy systems, placing maximum emphasis on superb customer interaction experiences and digitally enabling anything that moves.
Right now, the banks are surviving, not thriving; they’re rabbits blinking into the headlights of approaching traffic, frozen in the moment. But they need to disrupt themselves before others do it to them: change is painful but not as painful as the alternative. They have to do much more or they will see a decline in their fortunes due to their bankrupt capacity for innovation and their inflexible infrastructures.
Vietnamese National Citizen Bank Rises to Excellence with Three Global Financial Awards
Hanoi, Vietnam – Global Banking & Finance Review is proud to announce the sweeping victory of National Citizen Bank in the 2020 Global Banking & Finance Awards®. The bank was recently presented with three prestigious global financial awards: Best Place to Work Vietnam 2020, Fastest Growing Retail Bank Vietnam 2020, and Best Investor Relations Bank Vietnam 2020. The Global Banking & Finance Awards® recognize the innovation, enterprise, method, progressive and influential transformations that transpire every year within the global finance community. National Citizen Bank would like to extend their thanks and appreciation to the community and their customers for their continuous loyalty and support throughout the last 25 years.
The National Citizen Bank was recognized for its all-inclusive professional working environment and ongoing staff development that enhances its internal communications and employee relations. Throughout the last 25 years, National Citizen Bank has focused on the core fundamentals of regulatory modifications with the underlying goal of dividing the volume of both business and administrative tasks. As a result of this, the bank has successfully strengthened its staff’s capacity to obtain, manage outstanding liabilities, and acquire assets to negotiate and retrieve capital efficiently and reliably.
When asked what allowed the bank to triumph against the fierce competition, Wanda Rich, Editor for Global Banking & Finance vocalized, “one of the key factors that stood out to the committee is that National Citizen Bank strives to maintain and maximize profit to shareholders through the implementation of stable, sustainable business operations and advanced production methods. The bank has also remained stable, positive, and had a high growth rate in all of its activities, which is not often seen; however, it clearly indicates how prestigious and overall accomplished they are. They should be exceptionally proud of all three awards.”
About National Citizen Bank
The National Citizen Bank was initially established as a rural bank in 1995 under the name Bank of Kien River. The bank optimized its competitive standing within the global financial industry, later transforming into an urban banking institution where they reinstated their name as the National Citizens Bank. With a team of highly professional financial experts and customer service representatives, the bank embraces each customer’s diverse needs to ensure customary, efficient, and trustworthy experiences from start to finish. Over the years, the bank has prided itself on its continued emphasis on risk management and global business relations with investors, customers, and partners. For more information, please visit the National Citizen Bank.
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