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5 Amazing Examples Of AI And ML In The Financial Sector

“What used to take months or years has been reduced to days or weeks,” – Frank Fallon, VP, financial services at AWS


The financial services industry has traditionally been a data production powerhouse, from stock trades to credit card purchases. Many banks and other financial institutions today use AI and other machine learning techniques to analyze this data and improve their services to clients.

The Financial Services Market is now well into the AI loop of the digital marathon, an Odyssey that began with the introduction of the internet and has carried businesses through a number of iterations of digitalization. The development of AI is shaking up the industry’s dynamics, fracturing the old financial institutions’ ties to one another, and making way for additional innovations and new business models.

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5 Amazing Examples Of AI And ML In The Financial Sector

My previous blogs were based on applications of AI in the financial domain or benefits of ML etc, but in this blog, we are giving you five live examples which you can correlate easily where AI/ML has been implemented and used in financial companies. We have picked up randomly 5 companies from the financial services domain which practice AI and ML and have crafted an easy tech for use.

  1. Biz2Credit Case Study

Reducing the turnaround time for loan approvals from 7-10 days to 24-48 hours using AI/ML

Biz2Credit also employs an in-house AI/ML model to evaluate an applicant’s credit standing by analyzing financial data such as bank statements. The cash flow analysis produced by the model is used to determine the business’s credit risk based on the applicant’s reported revenue, expenses, and seasonality. Biz2Credit boasts that its bad-loan percentage is one-third that of the industry average, allowing banks using its Biz2X platform to make more loans with less risk.

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2. Mastercard’s NuData Case Study

NuData employs passive biometrics to validate account holders’ identities by analyzing their digital profiles, which cannot be copied by another party, as opposed to traditional, static data like passwords, security questions, and dates of birth. NuData’s universal identification is only one example of a new product that extends the usefulness of device-based intelligence through the application of ML so that multiple users can be associated with a single account. Customers can still enjoy uninterrupted, secure access based on other parameters even if they upgrade their devices or delete their cookies.

3.CreditVidya Case Study

It’s designed to solve one of the bigger shortcomings of digital security.

CreditVidya Extends the Loan Market to Millions of Financially Excluded Indians. It uses payment data, financial behavioral data, and device data recorded on smartphones to assess an applicant’s ability and intent to repay a loan using the platform’s AI and ML. With a growing database of loan applications, AI  constantly refines the criteria used to determine an applicant’s creditworthiness. Loan acceptance rates have increased by 25% and default rates have decreased by 33% for CreditVidya’s lending partners, which include 55 of India’s largest banks and non-banking financial organizations.

4. State Farm Case study

It automates risk management for all use cases leveraging a serverless-first approach.

Developers, security teams, and executive leadership at State Farm, the largest Property & Casualty insurer in the United States, were all upset with the laborious, opinion-based, and time-consuming process of authorizing deployments for production. They aimed to streamline things such that the clearance process took as little time as possible while yet minimizing problems and ensuring compliance. This was achieved by adopting a serverless-first strategy on AWS and thereby creating an automated, data-driven risk management procedure for all use cases. Amazon Web Services (AWS) are heavily leveraged in this context, particularly Amazon Elastic Compute Cloud (EC2) and Amazon Virtual Private Cloud (VPC).

Read: Let’s Dive Deep Into Fintech Vs The Conventional Banking

5.WaFd Case study

Conversational AI helped WaFd Bank (WaFd) improve its contact center’s customer experience. New embedded finance applications and digital-only institutions have altered banking during the past decade. Traditional banks like WaFd needed to compete digitally to meet client expectations.

WaFd upgraded its contact center after redesigning its online banking service. WaFd built a conversational AI and voice identification contact center solution using AI/ML platforms and improved agent and customer experiences.

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