MANTA is a solution for companies that have loads of data: huge, complicated data warehouses. But are you wondering how exactly MANTA fits into the data warehouses of big financial institutions? Welcome to our series called MANTA for Industries: Finance.
Credit Risk Scoring
Financial institutions that offer consumer loans invest a lot of money, effort, and know-how from years of experience into the development of advanced credit scoring models. In most cases, only a few people in the organization know and have access to these algorithms.
One way to decode a credit scoring model is to collect a significant amount of customer data where credit scores are combined with variable credit factors and then use statistical methods to understand the model. These “dangerous” combinations of data are often present in BI solutions and data warehouses. Every BI solution should allow the separation of access to such data by properly categorizing data sensitivity and by enabling user entitlement setup. This is not easy to achieve, and it is especially difficult to verify if the setup is correct.
With MANTA, you can easily identify and visualize the components of BI solutions (for example, data marts) where unwanted combinations of such sensitive data are present, or you can analyze user data-access setup to see if there are direct or hidden and indirect ways to retrieve those data sets.
You can export lineage from MANTA that can then be used for BI security improvements. And you can even restrict the use of MANTA’s data lineage right in our native visualization. When you find such relations, you can then take them into account when setting up user access for different user groups and teams who have access to MANTA and monitor who has access to different parts of the lineage from different data marts.
Regulatory Compliance and GDPR
Another popular way to use MANTA in big banking institutions is to produce proof for internal auditors that your credit scoring models are well protected. With the enormous number of banking regulations as well as regulations such as GDPR, it is twice as important to have decent data lineage to show the auditor when he arrives. To learn more, read our GDPR article. (link)
GDPR has introduced many more threats pertaining to corporate internal data. For example, a customer may come and request that you honor one of his rights such as the right to be forgotten. In this case, you may need to use MANTA to find every single place in your company data marts where the customer’s data is being stored to make sure you delete every single one of those instances.
You may also need to anonymize data. And after using MANTA to find all the places where your data needs to be anonymized, you might want to use MANTA again to double check that there is really no way to identify that person.
Being able to keep track of your environment and changes in your credit scoring algorithms is beneficial for many different reasons. One of our favorite client stories is about a customer who attempted to sue our client for not approving his loan the first time he applied, only to be approved a year later.
Using MANTA, the client was able to automatically find the changes made to the scoring algorithms over the last couple of years and identify the change in the algorithm for calculating credit scores for loans. The ability to show exactly what had changed and when allowed the financial institution to win the court case, saving them billions of dollars.
And how could you use MANTA in your financial institution?
Any comments or questions? Let us know at email@example.com, or go ahead and schedule a meeting using the form on the right.