AI use cases WildFire implemented in finance

Client: Medium size investment firm

Problem: A medium size institutional investment company was looking to improve their investment strategies. They had following goals.

– Maximize client returns

– Minimize the time of research

Our solution: We worked with the investment management company to process large amount of unstructured data like earnings calls, SEC filings, social media and press releases. We implemented a NLP based solution which was able to convert all unstructured data into structured format. This information was used to improve their investment strategies.

Results: The client was able to improve their investment strategies and improve returns by 0.5%..

Client: Medium size investment firm

Problem: A large investment firm was looking to help their financial advisors with research reports.

– Minimize time need to understand the reports

– Maximize their client returns

Our solution: We worked with various stakeholders and their innovation team to process all 50K+ reports they generate annually using machine learning and NLP and send them alerts each time the firm’s research division issues a recommendation on one security or publishes a relevant report. The system would tell advisors which client portfolios may be affected and have them draft emails to inform the client.

Results: The investment firm was able to speed up the process and
benefit their clients with timely investment advice.

Client: A large bank

Problem: A large bank was looking to reduce the false alerts during AML process. It had following goals.

– Reduce false alerts

– Increase operational efficiency of AML process

Our solution: We worked with the firm to understand all the rules
they have to flag alerts on accounts for OFAC sanctions list. We looked at the historic data of false matches. Using machine learning, we built a system to minimize false alerts increasing the operational efficiency of the AML process.

Results: The firm was able to reduce false alerts by 60% and was able to improve the operational efficiency of the AML process.

Client: A large bank

Problem: A large investment firm was looking for a tool to assist their advisors. They had few different goals.

– Better investment advice

– Reduce workload of their advisors

Our solution: We worked with their teams to understand their data.
We complemented their data using external sources like social media, evaluating credit card transactions anonymously, satellite imagery of retail parking lots and other information to come up with investment advice based on client portfolio. NLP and various machine learning algorithms were used to build different models. The robo advisor was used as a tool by their financial advisors.

Results: The firm was able to save a lot of time of their advisors.

Client: A small investment firm

Problem: A small investment firm was looking to develop a chatbot to help their customer service teams to handle call volumes. They had few different goals.

– Better customer support

– Reduce level 2 and level 3 call volume which was expensive

Our solution: We worked with their technology and business to develop an AI based chatbot and using all their historic call data. The bot was used by their customer service to answer all member questions and was able to reduce level 2 and 3 support.

Results: The firm was able to deploy this chatbot in multiple regions in short period of time and saw significant gains in efficiency and better customer service.

 


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