Other parts of this series:
Our first blog examined why commercial banks should introduce cashflow management tools for small- and medium-sized enterprises (SMEs) as part of an overdue drive towards fully digital banking. Our second blog looked at the impact of COVID-19, while our third and fourth blogs examine how data-driven banking can help the people at the very heart of the commercial bank – the relationship managers.
A Data-Driven World
We saw in the previous blog that relationship managers (RMs) would greatly benefit from data-driven tools that would ensure they spend more time adding value and less time on administration.
Such tools would also help RMs better understand their customers. That is particularly important given that the typical RM works with limited customer insight, and instead relies mostly on intuition and experience. A 2018 study, for example, found that 63 percent of banking sales leaders said their CRM tools failed to help them understand their customers.
We’ve already examined lead-generation and next-best actions – two of the four crucial areas where technological solutions can help. This blog will examine the last two areas: credit-decisioning and training.
Loan approvals are at the heart of commercial banking, and when it comes to this process, the RM traditionally sits between the customer and the credit officer. Even though commercial banks have begun to automate some of the elements involved – bringing in credit scores or allocating clients to specific “swimming lanes” depending on the entity or their loan requirement – there is much about the credit-decisioning process that they can improve further.
In this process in particular, commercial banks are well behind their retail cousins. The internal data that commercial banks have available is a mine of information – yet many look at only the most basic client information like profitability, recent transactions, how long the firm has been a client and how much it wants to borrow. Harnessing the full power of the bank’s internal data ensures higher accuracy in lending and fewer defaults; it also means the bank lends more, which is an ever-present demand on its senior management.
Banks should also mine external data sources, above and beyond the standard credit bureaus that almost all are mandated to use. For example, they could view customer ratings on their client’s Amazon and Alibaba accounts, then combine that with feedback from social media as well as news stories about the client – all derived from some of the basic web-crawlers now available. This would provide far more data, giving commercial banks greater confidence about their clients and speeding up credit-decisioning. This process can be made even more efficient by using a credit-decisioning engine driven by machine-learning that is flexible enough to consume new sources of both internal and external data.
“Mining internal and external data gives banks more confidence about their SME clients, allowing them to lend more, cut risk and earn higher revenues.”
Such an approach can generate significant returns. A leading commercial bank in Asia, for example, recently empowered its RMs by putting intelligent cross-sell capabilities at their fingertips so that they could better target the SME segment.
It combined machine-learning models to capture signals like search criteria and business performance indicators from internal and external data sources, then fed those through Accenture’s Applied Intelligence Platform, which integrates multiple tools and functions to process and analyze data at scale. As a result, the bank generated more than four million leads for existing customers and new prospects, achieved a 40 percent increase in identified cross-sell opportunities and saw a 46 percent increase in loan applications.
The fourth element is training. Commercial banks can use advanced algorithms to analyse how their best-performing RMs interact with clients through, say, text and voice calls, then harness those winning traits to ensure that lower-performing RMs can improve their skills to get better sales and customer satisfaction.
Additionally, some banks segment their RMs based on their strengths in order to optimise their performance. This results in a customized performance-management programme, matching the needs or strengths of each segment – for example, adjusted KPIs or RM allocation – and helps in planning training programmes. As yet, however, no commercial bank has cracked this, but in other industries – like online marketplaces – this forms a key focus in their ruthless drive for ever-improving efficiencies.
Dashboards and Data Sources
There are two final points to note. The first is that data-driven information must be accessible to be valuable. One solution is an AI-powered RM dashboard that combines dynamic portfolio-level metrics, smart priorities, a client prospecting list, portfolio-level sales opportunities and account-level market sentiment in a single view.
The second point is that many solutions are built in Europe and the U.S. where numerous data sources exist that these assets can plug into. That’s not necessarily the case in APAC. Consequently, commercial banks in APAC should consider how best to integrate existing data sources before they embark on this journey, and should always challenge vendors to ensure their local data requirements are addressed.
Clearly there is a lot of room to improve how RMs in APAC are supported!
Our fifth blog will examine how commercial banks can take advantage of innovative solutions to automate their trade finance offerings – and why the time to do so is now.
 Gartner’s Bank on Purpose, “Unleashing High Impact Sales Enablement,” 2018. https://www.bankonpurpose.com/videos/unleashing-high-impact-sales-enablement/