Finance
Over the past decades, the use of big data has been mandatory in finance mainly due to the competitive edge it provides. The integration of big data has indeed revolutionized the finance finance by way of the ability to come up with fast analyses to be utilized in a variety of financial services that include but are not limited to the following:
- Risk assessment
- Real-time analytics
- Fraud detection
- Customer analytics and segmentation
- Predictive analysis for planning
Mindberg.ai team prides themselves in their unmatched hands-on experience in modeling and credit risk scoring for all types of financial clients.
We deliver predictive models to our clients on a platform-agnostic way so that they can implement it on any system, whether it is on premises or on cloud. The models are delivered with full documentation that includes:
- Model details,
- User acceptance test results,
- Back testing results,
so that our clients can present their decisioning rationale to any internal or external supervisory authority when needed.
Project Scope
Mindberg.ai delivers services to financial institutions by way of carefully staged project components.
- Data Management: This stage aims to build area-specific, cloud-native and scalable big data infrastructure for analytics, real-time scoring, decision management and monitoring.
- Scoring Services: At this stage of the project predictive models and optimization algorithms are built for the entire credit life cycle.
- Decision Management: With this stage, our cloud-native decision management platform is tailored to your business requirements. Besides, you will be able to obtain the strategies for each step of the credit life cycle.
- Limit Management: At the end of this stage, a Limit Management system will be developed based on applicant income assessment, and customer & product level limit optimization models combined with the applicant’s risk category.
- Simulation: During this stage, capabilities will be built around what-if scenarios, back testing on historical data and simulations on alternative risk and / or limit strategies will be conducted (where applicable).
- Monitoring: Once this stage is complete, you will be able to keep an eye on every decision asset you have using best-practice monitoring dashboards.
Project Delivery
Mindberg.ai closely supervises the delivery of projects for the highest efficiency and client satisfaction.
- The first phase of project delivery is an Assessment Study, which is done on the client’s site where we take a snapshot of the current status in terms of data infrastructure, data quality, data availability, data collection practices, data pipelines, analytics maturity, analytics organization, analytics governance, data and analytics talent, and other dimensions specific to the client’s organization and its objectives.
- Following the Assessment Study, we come up with a detailed Roadmap that enables us to cover the gap between the current status and the best practice while serving the client’s mid to long-term targets. The roadmap also includes a prioritized short-term delivery plan for the best use cases as well as a comprehensive task list that will include smaller projects, their objectives, and workforce requirements.
- Delivery is the final phase, which tends to be the most neglected part of a successful business plan. Mindberg.ai differentiates itself by offering several decades of delivery experience to build machine learning models, a cloud-native decision management platform, and an end-to-end big data infrastructure.
