Clientcase: AI-driven Commercial Credit Process

By Analytics, Banking, Clientcases, News

What they wanted
Our client wanted to improve their commercial credit process for real estate clients and transform it to be more risk-based, data driven and efficient. This market-leading Commercial Bank experienced the need to maintain their competitive edge and contribute to company-wide cost reductions. Also important was the objective of freeing up capacity of their Front Office and Risk team specialists. These experts should focus more on new business, on innovation and on the biggest risks. Their commercial credit process was highly manual and especially the credit risk reviews required a lot of back-and-forth, precious time and lots of information.

What we did
We started by quickly building the value case and aligning the required stakeholders. Next, we introduced AI to largely automate the annual credit risk review cycle that was taking up thousands of hours each year. The client’s credit specialists trained our AI algorithms to assess the need for risk reviews. We used the specialists’ input and feedback to design the total solution in such a way that it was transparent, interactive and customizable. During a pilot the proof of value was highly convincing and the enthusiasm among specialists and senior management grew further. Then we started to use the AI model in practice, harvesting value and lessons learned, while preparing and realizing the full IT, process and Organizational change.

What we achieved
Within a few months the first AI model runs in production and automates >80% of all credit risk reviews. It outperforms the experts consistently in accuracy and helps redirect €500k annually in manual FTE.

AI not only accelerates the process and reduces costs; it also provides whole new capabilities. Using the AI model our client can now monitor risk on portfolio level and case level continuously. The model can also be used for quick scans of scenarios to spot which cases likely need first attention should the real estate market, or an individual client’s circumstances change. Building out new decision models for other parts of the process is in progress.

What they said
“Initially I was doubtful about the benefits of AI in real estate financing. The results have now completely convinced me”  – General manager Real Estate Finance –

Become a true Data Driven Organization

By Analytics, Banking, News

In Commercial Banking it is increasingly important that business processes are digital, data driven and can leverage AI. In the current times of unexpected change we see this magnified. IG&H data scientists observe that organizations who already transformed their processes now truly benefit.

Commercial banks are confronted with a sudden wave of SME client requests, changed risk drivers and changes in risk profiles. Banks want to help and need to figure out what (temporary) policy changes would be meaningful for clients. And also, what the impact of specific changes would be on the bank’s business.

Those who have already transformed their processes are now able to handle this situation much faster and more confidently. Their business processes are already more efficient and more consistent. And in the current time of crisis they also prove to be much more Scalable, Transparent, and Adaptable and they offer more options for looking forward in a smart way.

Scalable
Digital, data driven business processes with a high rate of straight-through-processing and where decisions are made (partly) by AI decision models, require much less human effort. Therefore, they can deal more easily with peaks in workload, especially in times when human capacity may be limited.

This benefit can only fully materialize when there are no bottlenecks in other parts with a crucial dependency. This stresses the fact that individual point solutions are not the way to go. The effective way is a transformation to become a true Data Driven Organization in People, Process, Data and Technology.

Transparent
Monitoring the impact of the current situation on the client experience, on process performance metrics and on KPIs is much more accurate and near real-time in a data driven process. This facilitates communication and coordination throughout the organization and allows management to take more effective actions.

For example: Dashboards can quickly be shared to observe what is really happening. Such as which teams have the highest workload increase. Or where clients’ payment behavior is most impacted.  Analytics can be used to signal early warning indicators such as trends and significant deviations.

Adaptable
AI decision models and business rules can be configured easily to effectuate policy changes like (temporary) higher risk thresholds, lowering the weight of specific risk drivers, higher or lower maximum values, etcetera.

For example: It can be easier to change a few parameters in a risk review decision model, than it is to communicate such changes to whole departments of specialists and coach them to quickly and consistently execute these.

Smart forward looking
Finally, AI decision models can be used to ‘test out’ different scenarios and evaluate very fast the likely effects on individual loans and on portfolio level.

For example: Changing the values of specific risk variables along the lines of different scenarios and observing the predicted effects, is being used to zoom in on those clients who likely require first attention.

AI models can be a very powerful tool to provide insight in likely future outcomes. A data scientist and business specialist who understand how the underlying machine learning works and on what data it was trained can provide a range of quick scan insights within a very short turnaround time.

IG&H’s data scientists and banking consultants continue to work with clients (especially now) to transform commercial banking organizations to remain competitive and benefit from being a true Data Driven Organization.

Would you like to talk about what you can do while your processes are not yet as digital and data driven as you would like? How you can best take the first step? Or how you can leverage your first progress and truly turn the corner to transform into a Data Driven organization? We are ready to help you explore and make data work! Just drop me a note!

Mando Rotman
Manager Data Science IG&H
E: mando.rotman@igh.com

Acquiring own Data Science competencies

By Analytics, Casestudys

What were the client’s needs?​

Over the past few years, our client, a leading logistic service provider with 30,000 customers, has centralised its BI function and taken major steps in Data Management. With a view on the increasing speed of technological developments, the need to innovate, and the utilisation of its data assets, we received the request to assist the client with the structure of a Data Science Team. IG&H already demonstrated the added value and potential of Data Science in previous projects.​

What was our approach?​

Data Science is a team effort. A Data Science team includes a range of competencies and areas of expertise and thus various functions such as a Data Scientist or Data Engineer. The team interacts with other teams within a company, such as BI, IT, Marketing, or Sales. Building up and integrating a new team within an existing organisation has a significant impact and faces many challenges. ​

IG&H has its own experience in this regard during the implementation of our Analytics practice. Supported by the best practices that we developed during this process, we were able to assist the client in compiling their own Data Science team.​ In addition to structuring the team, we also focused heavily on the collaboration with other teams and on the management of the new team.

What did we achieve? ​

In a period of 3 months, we have laid the foundation for a new competency that adds value to the organisation right from the word go. This enables the client to lift its provision of services to a higher level and, even more importantly, to remain competitive and relevant in a rapidly changing market.​

Granular share of wallet data for all major product lines for truly data-driven sales management

By Analytics, Insurance, Uncategorized

What they wanted
Make data-driven choices in the broker market: that is what leading Dutch omnichannel insurers want to be able to do. Key questions are: Who are today and tomorrow’s leading brokers? Where do we stand in terms of both volume and NPS? How do we enhance our position to realize sustainable growth? To this end, they wanted to gather in-depth data on volume, movements, share of wallet, and NPS. Read More