A Bank’s Journey to Simplified Data Analytics

Background:

Our Client, a leading European bank, faced a challenge. Despite having a large analytics team, they had only a handful of data scientists. The need was clear: facilitate swift cooperation on tasks, test hypotheses rapidly, and integrate findings into bank products. 

The bank's current solutions were a black box to the primary consumers of analytics, such as the marketing and sales teams. The solutions were code based, reliant on a bunch of tools & technologies, making it obscure for the users and analysts who were not proficient with the same. 

As a result, despite wanting to use more advanced analytics methods, the company was left with no option but to stick to basic data wrangling. 

Goals:

  • There was a strong need to empower domain experts to get the best out of the data.
  • Data analysts (without in-depth coding knowledge) had to be empowered to conduct predictions.
  • Overall, there was a need to simplify automation of solutions, accelerate hypothesis testing and enhance inter-team collaboration & knowledge sharing between the power users and domain experts.

Challenges:

  • There was a need for rapid hypothesis testing before bank product integration.
  • The company was struggling to address key issues like customer segmentation, churn prediction, and gauging the response to marketing activities.
  • The existing solutions were not transparent for the primary users, leading to a knowledge gap.

Solution:

Datrics, a no-code data analytics solution provider, was called-in to bridge this gap. The bank’s data was quickly prepared and connected to Datrics’ proprietary platform. The security of Datrics platform made it easy to connect to the bank’s infrastructure seamlessly, without disturbing any existing compliance. 

The easy to use UI enabled a smooth onboarding process of the various stakeholder teams and the team was all set to make the best of their data.

The teams could quickly delve into problem solving such as customer segmentation, churn prediction, and campaign evaluation - without worrying about the rigors of coding. The no-code solution for data preparation, modeling and visualizations ensured that in no time the teams were able to get to the underlying insights. 

The pipelines created were easily replicable and modifiable for similar tasks in the future.

Result:

The Client was able to save up to 80% of time (5x speed up!) for hypothesis testing for power users (Risk and Marketing analysts, product owners) without active Data Science team involvement.

The user-friendly interface ensured that hypothesis testing could also be done by non-tech users. No manual work was needed for regular data updates. The model's API could be integrated seamlessly into other systems. Using Datrics, the Client could easily integrate with their custom-built visualization solution to craft dashboards.

Conclusion:

The Client’s partnership with Datrics marked a significant turning point in their data analytics journey. Before this collaboration, the bank grappled with complex processes and a lack of streamlined collaboration. Datrics, with its innovative solutions, not only simplified these intricate processes but also fostered a culture of enhanced teamwork and knowledge sharing among different departments.

This transformation has equipped them with the tools and insights necessary to make more informed, data-driven decisions. As a result, the bank has witnessed improvements in its operational efficiency and service delivery. This proactive approach to data analytics not only strengthens the Client’s internal operations but also translates into tangible benefits for their customers, ensuring a more personalized and efficient banking experience. 

In the ever-evolving world of finance, such forward-thinking collaborations are pivotal for institutions aiming to stay ahead of the curve and consistently meet the needs of their clientele.

Do you want to discover more about Datrics?

Read more

Streamlining LTV Calculations for a prominent European Bank

The Client, an European bank faced a significant challenge. Their existing process for calculating the LTV of customers was cumbersome, manual, and reliant on tools like Excel.

Improving Credit Scoring for the Challenger Bank in the UK

Datrics empowers risk analysts to have much easier access to the data sources, build reusable ETL processes, and do predictive analytics using state-of-the-art machine learning models.

How to reduce stockouts by more than 80%: the new case for Profit Whales

Marketing agency works with a sophisticated ML-powered framework that predicts the supply and demand for Amazon's fastest-growing brands.
BACKED BY