AI Financial Risk Prediction

Risk analysis is an important part of any financial undertaking. This method of analysis shows us explicitly what the outlying problem is. But many mistake, the quality of a ”Risk” to be something that is created by one singular issue. But, the question of risk is multilateral, it can take on any dimension. Risks aren’t only related to the internal workflow but they are also related to various other processes, which are both internal and external.

India

Client

Innovation Intelligence

 

CLIENT

Our client is an innovation intelligence company based out of India. They provide modules that do competitive analysis to find out where improvements can be made with the framework of any financial or business concern. These insights are incredibly important for selecting initiatives and actions which provide the most utility.


We were tasked by them to create a risk prediction analysis model which can distill information from various sources and orient them with the insights provided by the client. This provides a deeply rational computation of what could be determined as the absolute risks.
 

The focus of this model, functionally and fundamentally, is to provide the Chief Risk Officer with a set of tools that could sift through both internal process flow data and external public data for prediction. They are also provided with a Natural Language Processing based search engine to retrieve predictions and data in an instant.

 

CHALLENGE

The most complicated element of Risk analysis is its variant nature, which creates an massive array of challenges that need to be tackled.

  • Abundance of Information - The amount of data pertaining to risk information is available everywhere and all around. Hence, the problem here is to identify suitable sources and proper data scraping methods.

  • Volatility of Information - Risk information is ever changing, there’s a new development everyday. To absolutely integrate everything immediately within the system is very ineffective and could lead to instability of the model.

  • Need for Simple Retrieval - Rather having specialized data with a very massive overhead, a simple retrieval/query technique has to be adapted for ease of use and functionality.

 

SOLUTION

With the aforementioned perspectives in mind, we have arrived at certain solutions:

  • Organize and identify proper sources for web scraping and content crawling. The online sources are generally websites which contain relevant industry news and review sections/sites. These sources are recursively identified from internal and external sources.

  • Irrelevant and repeating content can be stripped away via Natural Language Processing and content scoring.

  • Relevant entities are then extracted, they contain referential and contextual information. The feedback scraped from other websites are integrated into this system as well.

  • The contents are then structured with proper metadata referring to the URLs where they were scraped from. They are then stored within a referential database which can be easily queried.

  • The risk factors are then recorded and scored to specifications. They are ordered in a manner which is also easy for alter search and retrieval operations.

  • The natural language queries are then translated to their machine retrievable equivalents. Then via a
    simple query action, we can arrive at relevant risks and their indices.

 

RESULT

The frequency of risk extractions are increased, with reporting done on at a rapid and a daily pace to the Chief - Risk Officer. The duration for risk extraction was around 8 to 10 working days, now it can be radically reduced to 1 hour or so. The NLP Search Engine with its easy retrieval and search indexing methods can provide in-depth answers to simple propositions of risk. This increase in efficiency and versatility is immensely positive for our client.

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