Inventive Credit Risk: A data enabled, client-centric, end-to-end approach
The credit process is as old as the Bank, and one of its core activities. Under the close surveillance of the regulator, it has become a complex set of activities, notably because of the volumes and criticality of the data that must be gathered and used along the chain.
It is all the more the case for Small or Medium Enterprises and Corporates: indeed, while the credit process for individuals is used to handle massive amount of structured data and has now automated numerous steps (one can now quite easily get a grant online with a few clicks), the process for SMEs & Corporate credit is somehow still operated in a tailor-made fashion (with credit numerous credit committees, fully manual data collection, analysis and decisions, silos between front, risk, middle and back office…).
For SMEs, as small companies, branches and subsidiaries, the challenge is to get accurate, recent and exhaustive data. For the larger corporates, even though data is massively provided, the multiple steps, silos and product options within the process generates churns due to lack of transparency and delays for the client.
In all these cases, data usage and management are the key to unlock fast, transparent, higher margin & lower costs credit granting.
Capgemini Invent’s Credit Risk offering aims at maximizing the value of data usage along the credit process.
Our approach is structured around different use cases that stem from the field: we have tested and implemented the different building blocks of this offer with our clients. Our focus is strongly put on the client benefits and thus on the end-to-end process transformation, supported by our range of expertise around data:
- Improve the material supporting credit decisions and fluidify data flows with Data Management and Data quality:
i.e. improving the matching of external databases such as Moody’s of Fitch with internal Corporates databases to score them with more precision - Free business analysts from basic delivery tasks with Smart Automation and Natural Language Generation:
i.e. automating data gathering and preparation of financial statements and generating first level analysis - Facilitating and improving the credit decision making process with Machine Learning and Active Learning:
i.e. automating credit requests sorting between plain vanilla and complex requests and/or automating credit decision proposals based on past and present data - Increasing market knowledge and identifying insights and weak signals in real-time with Sentiment Analysis used on big data:
i.e. providing analysts and Risk officers with real-time analysis of multiple external data sources unused yet (newspapers, articles, forums, sectorial reviews)
In addition to bringing tested and “field proof” use cases, the strength of our approach also lies in its strong focus on end-to-end, client-centricity. While the credit process navigates through many layers of stakeholders and entities (relationship manager, business analysts, risk officer, region officer, risk / compliance officers, middle-office, support, back-office…), we leverage on our field knowledge to ensure that bringing change in the credit process goes along with clarifying and simplifying it, rather than destabilizing operational teams.
To end with, Inventive Credit Risk is a living approach as we are constantly improving it with more use cases stemming from the interactions and mission we have with our clients.
