Retail and Commercial Financial Services


Well Fargo, a diversified financial services company founded in 1852, operates from 36 countries with a headcount of 269,000. This company, despite carrying a legacy of more than two centuries, today possesses USD 1.8 trillion in assets and could achieve a market capitalisation of USD 245 billion. The company provides banking, insurance, investments, mortgage, and retail and commercial financial services through 8,800 locations and 13,000 ATMs spread across the globe.

This San Francisco based financial services’ company with a 70 million customer base today embraces predictive analytics for hiring. For several years, the company has been using a predictive-analytics-driven talent assessment solution to ensure they get the right fit for the job position and selected employees continue in their employment with the company with a long-term plan. Likewise, Wells Fargo could screen over two million candidates over the years. With predictive analytics, Well Fargo is recruiting those who are more likely to perform better and stay longer. The company believes in the unique culture of ‘need based selling and customer service’. The recruitment process needs to focus on this aspect to ensure candidates who are hired are the best fit with the culture of the organisation.

The company needs to use predictive analytics to centralise recruitment functions and for standardising the selection process, performance review and employee incentive processes. Rather than using psychometric tests in recruitment, the company emphasised the use of biometric data. Analysing such biometric data, the company could know about the number of jobs candidates had, the duration of stay in each job, the number of promotions the candidate had, the highest level of education, etc. Using these inputs, the predictive analytics solutions of Wells Fargo can predict the best candidates based on the analysis of their background experiences, career motivation, performance, life/work skills and so on.

Predictive models developed by the company could demonstrate high reliability for predictive future performance of the recruited employees. Any candidate seeking a job position in Wells Fargo needs to answer a set of questions online. These questions seek biometric data and also test the functional knowledge and skill of the prospective candidates. The predictive solutions are so built that any candidate scoring high (as considered by the company) in his/her interview automatically gets scheduled. The prospective candidate can accordingly meet Wells Fargo’s representatives on the scheduled date, time and venue. This process also could substantially reduce the cost of recruitment and selection.

Even with this, the company could identify who are the team members for future leadership positions and accordingly groom them right from the beginning with different onboarding processes, assigning special coaches and mentors, and so on.

Rather than making these predictive models one size-fit-all solution for recruitment, the company also analyses the statistical significance on an ongoing basis and could get the results even at 90 percent confidence levels. With improved performance and retention, the company could make predictive recruitment solutions its basis for hiring.

The company currently is coming across multiple issues, with COVID 19 situation and with a global presence, 36 countries. The pandemic has impacted different countries in different ways, and Well Fargo will have to come up with descriptive analytics to deal with the issue of pandemics and issues of manpower planning.

There are issues of employee engagement and high employee turnover. The HR business partners are struggling to increase employee engagement and retain employees. There could be multiple reasons for lack of engagement and high turnover. There is a need to do a root cause analysis of the problem of lack of employee engagement, high employee turnover.

Create a report for the senior leadership with root cause analysis of the problem. Your report should,

1. Develop descriptive analytics for the manpower planning issues around pandemics.
2. Develop predictive analytics about the issue of retention and employee engagement.
3. Develop strategies and make recommendations for high employee engagement and retention.

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