#149 Delivering Business Impact with Data & AI in Financial Services with José Murillo - Chief Analytics Officer
We are joined by José Murillo, Chief Analytics Officer at Grupo Financiero Banorte, Mexico's second largest financial group and also the most profitable one pre-pandemic.
José had a long tenure at Mexico’s Central Bank, where he worked at the research department. Around 7 years ago, he joined Banorte and established the Analytics Business Unit. His efforts delivered profits equivalent to 46x the cost just within the first year and almost 250x their cost last year. During his time there, he has helped the bank go from the fourth to the second largest financial group in Mexico, surpassing companies like Grupo Santander and CitiBank.
His group has been recognized as a success story within the data and analytics industry. The value created with analytics in 2020, the sixth year of operations of his group, was equivalent to 1 billion USD –during his tenure the value created exceeds 4 billion USD. This case was published at Harvard Business Review and Forbes as an example of a company which made its analytics investments pay-off.
Stay tuned to learn more about the artificial intelligence applications he has developed and the digital transformation José has achieved at Banorte.
Enjoy the show!
We speak about:
[3:50] Tell us about your role in the organization and your tenure within the bank.
[14:35] Why did you choose to start on the credit card side?
[18:52] How has your team and capabilities evolved?
[21:30] Tell us about the operational processes around the project deliverables, how is that structured?
[28:24] How was the process of building that experimentation platform?
[36:00] What is the current split between the three types of projects and could you let us know some examples of each?
[43:25] Regarding data science applications in the banking industry, what do you think will be a game changer in the industry?
[46:50] Applications of analytics in customer experience.
[50:45] Have you done analytics with millenial spending models?
[53:30] If you could magically improve the data science technology available, what would be the technological improvement?
Resources:
Jose’s LinkedIn: https://www.linkedin.com/in/jose-murillo-b676261a/
Grupo Financiero Banorte on LinkedIn: https://www.linkedin.com/company/grupofinancierobanorte/
Quotes:
"The idea was to build this data science team and I think it was a blessing in disguise at the time, in the sense that they funded the group, but I think there were still some doubts of what would be the yield of bringing people different from what the group used to have. They said "Yes, we are going to fund it but in a year time you need to deliver 10x your cost. It seemed quite steep because for a traditional business they were asking 3x the cost. Long story short, within the first year we delivered 46x the cost and last year we were close to 250x our cost."
"We were built to deliver profits and I think that helped us focus on things that were valuable to the organization and to be accepted because we were bringing value."
"I’ll be honest, I was a bit overwhelmed when they told me that I needed to make 10x my cost in a year and I was a bit reluctant to accept the challenge. My boss told me ‘you’re not going to have a problem, just go and look at the amount of resources we have on the credit card business. You’ll be fine.’"
"That year that they worked with us, just that first project increased their profitability by 25% and it had recurring benefits in the following years."
"We now have a factory of ideas that is very efficient. The first RCT, randomized control trial, that we wanted to run, took about 6 months to get it authorized and get it done. By now it takes us around 48 hours from the time that we think of the thing and we deploy and then we have all the right algorithms to measure. The things that are successful we scale them up using causal machine learning algorithms and it’s really a thinking machine that has been built. For that, you need people that are from backgrounds closer to social psychology, economics and then you also need the people that know about computer science, artificial intelligence and that also know about experimental techniques to understand what is the true causality of interventions that you are running."
"If you have an organization of 27,000 people you don’t want to have just one brain thinking, you want to take advantage of all the brain power the organization has."
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