70%
Reduction in report generation time
50%
Improvement in data accuracy & quality
40%
Increase in employee productivity
As a leading financial institution in Egypt, CIB operates in an ever-changing landscape where being ahead in data-driven decision-making, operational efficiency, and customer-centric innovation help maintain a competitive edge. The bank faces challenges like integrating advanced analytics into actionable insights, streamlining complex workflows, and addressing the growing expectations for faster, more convenient financial services.
With growing competition from other institutions and higher consumer expectations, CIB has come to understand that they need to adopt automated solutions to enhance customer satisfaction, reduce industry and organizational inefficiencies, and continually innovate in banking.
First, with the promise of predictive modeling and AI bringing the potential to revolutionize decision-making, a gap has appeared between technical complexity and business understanding, becoming a barrier to actionable insights. Issues surrounding key components to a successful data strategy — integration, workflows, and retrieval — make it hard for banks like CIB to create timely, accurate reports without automation.
Next, CIB’s contact center has struggled with high support volumes, which has driven up costs. Complex customer questions have required them to shift from reactive to proactive support.
Finally, to onboard new members, especially in lending, CIB has found difficulty because of manual processes, the lack of credit histories, and growing competition.
All three of these problems require solutions that use complex machine learning (ML) and AI while also balancing compliance and risk factors. CIB understood that to solve these challenges and remain competitive, they needed to find scalable, integrated, and automated ways to enhance the customer experience while also increasing operational efficiency.
As more and more industries come to depend on analytical models to generate insights and make data-driven decisions, organizations gain deep knowledge into various aspects of their business, from understanding customer behavior and predicting volatile market trends to optimizing internal operations and forecasting financial performance.
But even with the concurrent improvements in model quality, a gap has always existed between the highly technical nature of how AI and statistical models work and the ability of even seasoned business professionals to interpret and take action on the insights they produce. Reporting and insights must be as high quality as possible to keep up with the finance industry and allow for informed decision-making. However, banks tend to have harder times meeting deadlines without sacrificing quality.
To solve for this, CIB believed that a strong, automated pipeline was critical to producing the results needed for data science use cases. This would bring the consistency and accuracy needed, while also effectively delivering on-demand reports. In addition, the solution needs to be able to scale, be easily integrated with their current data infrastructure, and decrease dependence on manual processes.
To tackle the difficulties of providing insights and data science with on-demand reports, CIB built the InsightGini app with Dataiku. Their solution includes a newly-built, completely automated, and thoroughly validated analytical layer on top of Dataiku. Data standardization and primary application source are both handled by this layer.
By lettering users enter a column name or description, the tool, built with Dataiku, streamlines the report generating process. — Mohamed ElDesouky, Analytics Data Manager, CIB
The input is matched with the most relevant columns in the analytical layer using a fine-tuned LLM model (paraphrase-mpnet-base-v2) that was trained on their proprietary data. Users can choose the columns that are most often utilized based on the model's suggestions. If further filters, like date ranges, are required, the tool will also ask the user to apply them. Following the user's selection, the app's backend processes a query utilizing a relational table that stores the join combinations from all analytical layer tables. This query is then used to generate the final view. Users are able to effortlessly access the insights after this final view is made available on the app interface.
CIB has seen dramatic transformation even in their daily operations, particularly in how insights are generated and used in their organization. Specifically, they have indicated improvements in key areas:
By analyzing behavioral data for our customers, our Data Science team built a model leveraging Dataiku platform in order to accurately predict successful outcomes and provide personalized product recommendations. — Nelly Youssef, Head of Data Science and Advanced Analytics, CIBAdding the integration of a predictive model into the call center agent workflow streamlined their interactions. As they received real-time information about call potential and offered upsell recommendations, agents were able to tailor their calls to each customer.
Ultimately, Dataiku’s role in the successful launch of this tool extended beyond its technical features. It served as a catalyst for innovation and improved customer engagement. — Nelly Youssef, Head of Data Science and Advanced Analytics, CIB
GET TO KNOW CIB
FOUNDED IN
1975
TECH STACK
Dataiku + Cloudera
KEY CAPABILITIES
Machine Learning, AutoML, AI Ecosystem, GenAI & Agents, Analytics & Insights
A third area that required attention at CIB was that of streamlining the lending decision process for new customers — a historically time-consuming one. In a fast-moving banking industry, the traditional methods of onboarding new borrowers has involved significant amounts of paperwork, income verification, and manual intervention, leading somewhat to delays and customer dissatisfaction.
In addition, many first-time borrowers lack comprehensive credit histories, making lending decisions difficult. CIB spotted an opportunity to not empower their risk teams, but also design a model to automate and expedite the credit process while ensuring regulatorily compliance. They needed a New-to-Bank (NTB) income imputation model to automate and expedite the lending onboarding process while maintaining compliance with regulatory and risk standards. This model would predict the income of customers who are new to the bank and have no or limited credit history.
One of CIB’s main reasons for opting to partner with Dataiku was because of the platform’s ability to facilitate collaboration across different roles. This particular use case and challenge involved a cross-functional team, each with a distinct role: Data engineers managed data pipelines, data scientists handled model development, and risk analysts validated model output and monitored model performance.
CIB’s Data Science team also took advantage of Dataiku’s data manipulation and exploration features for advanced feature engineering. They transformed selected data fields into meaningful features using aggregations, normalization, and other techniques, then fed these into a multi-classification ML model to estimate a customer’s income tier. A second model was developed to run in tandem to ensure that no opportunities were lost with first-time borrowers as well.
Dataiku’s main strengths of ML automation and functional UI helped the team test different algorithms and compare performance across multiple iterations. CIB tracked multiple performance metrics, helping them make data-driven decisions and decide which models were the most effective. They then used Dataiku’s deployment features to push them into production.
The introduction of this model has revolutionized our daily loan operations, particularly in the customer onboarding process. Previously, onboarding required significant manual labor, with teams manually verifying documents, estimating income, and assessing customer eligibility. This led to long processing times and increased operational costs. Today, the entire process is automated, given the integration of our in-house machine learning model using Dataiku into our digital lending platform. — Nelly Youssef, Head of Data Science and Advanced Analytics at CIB
Finally, the platform’s ability to create APIs was crucial in integrating the model into CIB’s digital lending platform, resulting in real-time predictions. This approach opened the door for CIB to deliver instant income estimates during the customer onboarding itself, improving the experience for both the bank and the customer.
Before the implementation of ML models within this business scope, onboarding new customers required teams to manually verify documents, estimate income, and assess customer eligibility. This led to inordinately long processing times and increased operational costs. After automating the entire process leveraging Dataiku, customers can submit their information, then the system takes care of the rest: everything from gathering customer data to estimating income, and assigning a credit limit.
The increased velocity that CIB can move is easy to see as well — because the model can operate in near real-time, onboarding time has been reduced from days to minutes. Because they can onboard so quickly, they can take on more customers in less time, expanding reach and market share in a very competitive lending space. With a high level of customer satisfaction and the ability to move with agility, CIB has gained a powerful strategy in the microfinance space where speed is critical to success.
The customer segments that CIB has reached is a fascinating result of implementing AI and ML into their onboarding strategy. Typically, customers with limited credit history are often overlooked by traditional banking processes. The systems CIB Data Science team built in partnership with Dataiku have allowed them to tap into these underserved customer segments — even with little customer information on hand, their tools can still estimate income and provide insights into which credit products they can offer.
One of the key factors contributing to CIB’s success on this project was Dataiku's seamless integration with existing systems which created a unified view of the bank’s operations, providing a solid foundation for data-driven decision-making. This integration streamlined processes and reduced manual tasks, leading to increased efficiency and cost savings. — Nelly Youssef, Head of Data Science and Advanced Analytics at CIB
The tools that CIB developed couldn’t have been built without the extensive collaboration enabled through Dataiku. Smooth communication pipelines and code sharing between team members made managing model versions and choosing the best models for deployment much simpler. The interface made it easier for non-technical stakeholders to monitor performance and stay informed. Everyone from data scientists to risk managers worked together against shared targets, delivering a model that ultimately transformed their entire customer onboarding process.