AI in Sales: How Banks are Relying on Artificial Intelligence to Improve Sales
Over the last few years, technology adoption within the banking industry has proceeded at a breakneck pace. We’ve seen technology-first upstarts increasingly challenge the traditional hegemony of large banks. From digital wallets to digital banks, fintech companies are seeing a huge opportunity being left behind by banks to improve customer experience and speed of execution while containing costs of operations. To counter this threat, banks are stepping up to the challenge and are embracing technology that promises to improve topline revenue while containing costs of execution and maintaining the level of service that their customers have come to expect.
Technology reliance is rapidly increasing in the banking sector. New technologies are fast entering the realm of banking promising to build the future of the bank. One of those technologies, of course, is Artificial Intelligence.
AI in Banking
Artificial Intelligence holds the promise to massively transform the banking industry. With the quantum of data that banks tend to have about their customers, their demographic information, their transaction histories and their credit ratings — the potential for AI is immense in the banking realm. In other areas too — such as managing risk, detecting fraud and authorizing credit — banks are seeing immense value in using AI techniques to detect fraud faster and with greater accuracy.
For customers too, banks are looking at AI-powered solutions to improve customer experience. Many banks are piloting natural-language based chatbots that can help customers understand their balance, their spends by category and provide product information over a simple interface.
Then there is the whole aspect around the possibilities for AI to contribute to sales. Let us do a deep dive into some of the areas where AI is contributing heavily to improve and sharpen the efforts of sales teams in banking.
AI in Banking Sales and Relationship Management
Sales in the banking industry can be extremely complex. The larger banks have a massive portfolio of customers, a huge number of products and plans and a wide network of internal and external folk they engage to market, sell and administer their products. Sales and relationship management teams can utilize AI to tie this extremely complex machinery of customer segmentation, prioritization, and acquisition through intelligent interventions and recommendations.
Prospect and Customer Engagement
Banks have put up multiple web and mobile properties where they advertise their products to customers with the objective of acquiring them as customers. And numerous customers who do their own diligent research into which products they wish to buy or invest in while evaluation competitor propositions.
With AI, we can now have a much more intelligent way to engage customers in the moment of truth when they are browsing for products to purchase. By sensorizing the digital properties where all the product information is provided, virtual assistants can help move the purchase along. What virtual assistants powered by AI typically do is that they monitor customer journey patterns over a period of time and identify which particular intervention has the highest likelihood of conversion. So depending on how the customer is browsing on the web or mobile property and the questions he is asking the bot, the agent can take an intelligent decision to offer more product information, discounts, competitive comparisons or connect to a human sales agent for a deeper discussion.
Prioritizing Customer Lists based on Propensity to Buy
As I wrote earlier, the sales process in banking has multiple moving parts making it remarkably complex. Banks have anywhere between hundreds of thousands to millions of customers. Is it truly scalable (or even desirable) to call each one and bother them with products which they are clearly not interested in purchasing? This approach is not only unscalable and low-yield but also leads to disgruntled customers.
The alternative is to engage a smarter methodology using AI. Artificial Intelligence algorithms can be trained to look for certain characteristics of customers who are most likely to buy. Using historical observations and continuous learning, AI can be employed to constantly identify high-quality leads that showcase some typical behavioral patterns and highlight them to sales teams to pursue.
Prescriptive Recommendations
In the first two points, we discussed how AI can be used to engage customers and prioritize those that have a higher propensity to transact. The third piece then is to match prioritized customers with the right products. Here too, AI can be transformational for the sales process.
Once again, by training the algorithmic models within AI, we can look at previous and current purchase patterns of customers and identify which products from the bank a customer is most likely to invest in. At a human-level, the best we can do is assume that a high net-worth individual with a higher risk appetite is most likely to invest in high-risk high-return funds. However, AI can find patterns at a more granular level, look at multiple data points beyond demographics to match the fund with the right individual. Additionally, AI can also recommend the method of outreach that an individual is most likely to respond to based on his or her behavioral profile — email, phone call or an in-person meeting and prescribe salespeople how best to acquire a new customer for their product.
Artificial Intelligence provides a plethora of solutions for the banking industry in general and for sales in particular. Operationalizing this AI-led transformation requires banking leaders to first identify and create the case for which processes at the bank are most attuned to an AI-powered intervention. They need to do the required business due diligence around the quantifiable business value that can be delivered through AI, alongside a technical assessment if the right data and people are available to realize this change.
