Step 1: Define quantifiable metrics
The bank had three distinct goals:
1) Improve the effectiveness of its sales force in growing the pipeline
2) Increase operational efficiency so relationship managers could successfully handle all new prospects
3) Deliver a superior customer experience
We began by translating those goals into four quantifiable metrics:
- Time spent by bankers on prospecting
- Percentage of deals closed – the number of customers taking a loan after being qualified as potential prospects
- Loan closing cycle time
- Cost per loan originated
Step 2: Set baseline for current state
We manually collected data and conducted interviews to measure the current state. Then, we attached realistic targets to each of the four metrics. This helped us to understand the gaps. And for the first time, the bank gained baseline visibility into every stage and subprocess in its commercial lending function.
Step 3: Identify constraints
Next, we pinpointed more than 30 factors across six distinct areas that were keeping the bank from achieving its goals. These areas included inefficient credit process execution, suboptimal underwriting due diligence, partially automated workflows, data duplication, lack of clarity in roles and responsibilities, and process variation. To help prioritize, we ranked each one of the more than 30 factors as high, medium, or low impact.
Step 4: Recommend solutions
After that, we generated several comprehensive options covering the end-to-end commercial lending process. Each option featured an operating model, process, and intelligent automation components for a best-in-class transformation. Our recommended solutions included automating credit memos using natural language generation, automating financial spreading using our artificial intelligence platform Cora LiveSpread, and robotic process automation for a prospect-profiling dashboard. We also developed a model that showed the bank the quantifiable impact of each recommended solution on the four defined metrics. See figure 3.
Step 5: Establish implementation plan and cost-benefit analysis
Finally, we created a recommended implementation roadmap for each of the solution options. To make it easy to implement, the plan spanned two years and contained a breakdown of high-, medium-, and low-impact components into six-month increments. In addition, we developed a financial impact model that allows the bank to calculate the cost and benefit of each solution, so that it could prioritize which to implement.