Untitled design
Try Now

Key Use Cases

Use Cases

Practical Applications of Disparity Analysis

Practical Applications of FairBorrow™.Ai

FairBorrow™.Ai enhances decision processes by reducing inconsistencies. Explore various use cases below or share your specific scenario for customized analysis.

Any decision process can benefit by reducing inconsistencies and can be used with FairBorrow™.Ai. The use cases described below are only a few of the possibilities. If you have a specific use case that you would like to try with our data, please contact us. 

Home Lending (Mortgage) Decision Process

FairBorrow™.Ai addresses mortgage industry challenges with regulatory compliance, data solutions, and infrastructure options.
Read More

Credit Decision Processes - Auto Finance, Credit Cards

Improve credit decisions with FairBorrow™.Ai using custom solutions in confidential, secure collaboration with financial institutions.
Read More

Anti-Money Laundering and Bank Secrecy Act

We improve AML/BSA processes, offering insights and leveraging decision anomalies for improvements in institutions.
Read More

Home Lending/Mortgage Decision Processes

Driven by the size of the mortgage industry in the USA, and Fair Lending-related regulatory actions, FairBorrow™.Ai is uniquely positioned to help address one of the most challenging aspects of the Financial Services industry.
Read More

Credit Decision Processes - Auto Finance, Credit Cards

Credit decision processes face similar challenges to those seen in Home Lending, and will benefit from enhancing decision consistency. For such use cases, a Financial Institution may confidentially and securely provide proprietary data for a customized solution.
Read More

Anti-Money Laundering and Bank Secrecy Act

Financial Institutions are required to have an AML/BSA Program, and often implement automated controls within their processing systems to create alerts. These alerts must be reviewed by AML/BSA personnel, and the decision made to either close them as false positives, or create a Compliance Case for further investigation.
Read More

Optimizing Stock Trading Decision-Making

Stock trading compliance, Securities and Exchange Commission(SEC) insights, and anomalies for consistent, improved financial processes.
Read More

Insurance Policy Underwriting Decisions

FairBorrow™.Ai enhances insurance underwriting, providing insights and leveraging decision anomalies for process improvements.
Read More

Insurance Claims Adjudication Decisions

FairBorrow™.Ai analyzes insurance claims, offering insights on anomalies and areas for improvement, enhancing consistency in decisions.
Read More

Optimizing Stock Trading

Stock trading must adhere to a Financial Institution’s policies and procedures. Despite employing automated modelling, the industry has seen significant regulatory action due to potential insider trading.
Read More

Insurance Policy Underwriting Decisions

Similar to the loan application approval process, an Insurance Policy underwriting decision is based on various factors such as coverage requested, and despite the use of automated models can still suffer from inconsistencies due to judgmental processes around use of such automated models.
Read More

Insurance Claim Adjudication

Determining the outcome of an insurance claim is often a complicated process, which considers multiple factors such as an appraiser’s estimate and policy coverage. FairBorrow™.Ai can consume data related to insurance claim adjudication and provide critical insights, particularly where anomalies and inconsistencies exist.
Read More

Headquarter

Exploring machine learning or have a specific use case? Let’s talk.

WhatsApp_Image_2024-02-06_at_10.40.27_AM-removebg-preview
Facebook-f Icon-linkedin Twitter Youtube
Copyright © 2024 FairBorrow AI, All rights reserved.

Headquarter

Exploring machine learning or have a specific use case? Let’s talk.

Untitled design
Facebook-f Icon-linkedin Twitter Youtube
Copyright © 2024FairBorrow™.Ai | All rights reserved.

Home Lending (Mortgage) Decision Process

Driven by the size of the mortgage industry in the USA, and Fair Lending-related regulatory actions, FairBorrow™.Ai is uniquely poised to help address one of the most challenging aspects of the financial services industry.

A fully implemented solution using publicly available Home Mortgage Disclosure Act (HMDA) Data from the Consumer Financial Protection Bureau (CFPB) is available for users. In addition, a financial institution may confidentially and securely provide additional proprietary data for a customized solution. Such solutions can be implemented using FairBorrow™.Ai infrastructure, or within the financial institution’s domain.

Other Credit Decision Processes such as Auto Finance, Credit Cards

All credit-decision processes face similar challenges to the home lending decision process, and will benefit from enhancements of decision consistency. For such use cases, an auto financer or financial institution (in the case of credit cards) may confidentially and securely provide proprietary data for a customized solution. Such solutions can be implemented using FairBorrow™.Ai infrastructure, or within the financial institution’s domain.

Anti-Money Laundering (AML) and Bank Secrecy Act (BSA) disposition of alerts

Financial institutions are required to have an AML/BSA Program, and often implement automated controls within their processing systems to create alerts. These alerts must be reviewed by AML/BSA personnel, and decision must be made to either close them as false positives, or create a compliance case for further investigation. The compliance cases, upon further investigation could result in filing Suspicious Activities Reports (SARs) and/or Currency Transaction Reports (CTRs), or the case is closed as not requiring any further action.

The process described above is complex and requires expertise-based judgment at multiple steps. It is also a great example of how even though alerts creation is via an automated rules- based model, the final disposition could still have significant inconsistencies.

FairBorrow™.Ai can consume data related to AML/BSA processes confidentially and securely within a financial institution’s domain and produce deep insights as well as identify areas where decision anomalies can be leveraged for process improvements. In addition, much like the case of an underwriter in credit decisions, it would be possible for an AML/BSA team member to check their decision for consistency from within such a process.

Stock Trading Decisions

Stock trading must adhere to a financial institution’s policies and procedures. Despite employing automated modelling, the capital markets industry has seen significant regulatory action due to potential insider trading. The Securities and Exchange Commission’s Enforcement Results for 2023 state that in fiscal year 2023, the SEC obtained orders for $4.949 billion in financial remedies, the second highest amount in SEC history, after the record-setting financial remedies ordered in fiscal year 2022.

  • The financial remedies comprised $3.369 billion in disgorgement and prejudgment interest and $1.580 billion in civil penalties.
  • Both the disgorgement and civil penalties ordered were the second highest amounts on record.
  • The SEC also obtained orders barring 133 individuals from serving as officers and directors of public companies, the highest number of officer and director bars obtained in a decade.

 

Anomalies in trade executions can provide actionable intelligence on how to improve the process and which specific areas should receive a heightened focus, resulting in more consistent trading. For such use cases, a financial institution may confidentially and securely provide proprietary data for a customized solution. Such solutions can be implemented using FairBorrow™.Ai infrastructure, or within the financial institution’s domain.

Insurance Policy Underwriting Decisions

Similar to the loan application approval process, an insurance policy underwriting decision is based on various factors, such as coverage requested. Despite the use of automated models, insurance underwriting can still suffer from inconsistencies due to judgmental processes around use of such automated models.

FairBorrow™.Ai can consume data related to insurance policy underwriting processes confidentially and securely, either within an insurer’s domain or within FairBorrow™.Ai, and produce deep insights while pointing out where decision anomalies can be leveraged for process improvements.

Insurance Claims Adjudication Decisions

Determining the outcome of an insurance claim is often a complicated process, which considers multiple factors such as appraiser’s estimate, policy coverage, etc. FairBorrow™.Ai can consume data related to insurance claims adjudication and provide deep insights, particularly, where anomalies and inconsistencies exist. These insights can help a risk manager identify which areas need improvements.

In addition, a claims adjudicator can check for consistency of decisions and garner insights that help them improve their own performance via improved consistency.