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AI Augmented Business Risk Monitoring

Lenders and venture capitalists consider only historical data and lagging financial indicators, and sometimes inventory in their risk calculations and predictions, neglecting the market volatility - due to which they get an incomplete picture of their borrowers.

In recent research, a substantial effort has been invested to develop sophisticated financial polarity lexicons that can be used to investigate how financial sentiments relate to future business performance rather than working with general know-how and intuition.

Our financial early warning and monitoring system that is designed to provide these insights for lenders and investors. Along with financial & historical metrics, this system will daily monitor, capture and analyze leading indicators of distresses present in the business entities and ranks them according by risks, to take final buy, sell or on-hold decisions making the future visible to the present.

California, USA


Governance, Risk, and Compliance Industry



Our client, based in California, has been working in the field of providing AI-based GRC support tools for many major BFSI businesses for the past 3+ years. Knowing our expertise in AI models, we were tasked to create this Business Risk Prediction and Monitoring model to provide systemic insights for our client to streamline the decision making for lending, acquisition, and liquidation business processes.


The obligations provided by our model is derived utilizing historical and social metrics to create a ’Quantitative Risk Index’ for every monitored entity in the pipeline. This index, being quantitative, can provide the decisions that have to be made based on the degrees of viability of the business targeted.



Our model needs to tackle these challenges that were identified by our field analysis :

  • Abstraction of Data - The generic financial, credit, news and other data used for the base for the model can be very hard to contain and analyze, it has to be abstracted using a proprietary methodology.

  • Data Veracity - The data used had to be truthful and distinct; irrelevant, redundant, and fake data could be extremely harmful for our prediction model.

  • Parsability of Inference - The results produced should be easy to parse and should be able to integrate with the preexisting decision making framework of the client. It shouldn’t create deviations and distractions from our goals.



With the challenges in mind, we have created flexible solutions to deal with them:

  • We monitor social media posts/feeds (Twitter and Reddit), selective business blogs, news sources, credit & aggregration bureaus, reports, and review sources; which can then be categorized, filtered, and compartmentalized to find their authenticity and relevance by our AI model.

  • The relevant metrics are then summarized and organized in accordance to the specific sentiments based on the inherent contexts (good, bad, and etc.) that is contained in them.

  • These sentiments provide the base for the creation of a Business Sentiment Index which can be correlated to the business distresses since both are concomitant in nature.

  • This correlation provides the angle to calculate the Risk Index out of the Sentiment Index. Since businesses with a lower sentiment always engender a liquidation or a bankruptcy, the Risk Index in turn provides us with the Support Model for arriving at a buy, sell, hold decision.



  • Our prediction model prevents wrongful undertaking of risks which could put the companies involved in a dilemma.

  • Predictions and highlighting of red-flags events even of the lower rung business sentiment index can be efficiently done as well.

  • Pinpointing and forecasting risks is also a very important aspect of model. Even Risks that could stem our own client can be prevented using our AI prediction model which could identify relative problems in their own business model.

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