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FairBorrow™.Ai

Artificial Intelligence for a good cause

Leverage the latest GenerativeAI features to gain critical insights and enhance the consistency of your decision making process.

Key Users

Ask us about custom solutions to meet your specific needs

At FairBorrow™.Ai, we analyze large and complex data sets along with the power of Artificial Intelligence (AI) in developing deep insights. In particular, our proprietary data management process helps to enhance consistency of decisions, such as loan approval and underwriting decisions. Enhancing consistency of decisions could lead to reduction in bias and thus help reduce and even eliminate potential regulatory issues, such as Fair Lending violations and their resulting adverse financial and reputational impact.

At FairBorrow™.Ai, we analyze large and complex data sets using advanced Data Management techniques such as Artificial Intelligence (AI) in developing deep insights. Our proprietary data management process helps to enhance consistency of decisions, such as loan approval and underwriting decisions, potentially leading to a reduction in bias and thus helping reduce or even eliminate potential regulatory issues, such as Fair Lending violations and their resulting adverse financial and reputational impact.

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Key Users

Core Concepts

Fair Lending

Practical Applications

Years Experience
0
About FairBorrow.ai

Driving Insights Through AI Innovation

We harness AI to unlock profound insights from public data, including HMDA, ensuring our proprietary data management fosters decision consistency, minimizes bias, and addresses regulatory challenges for financial integrity.

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Who should use FairBorrow.Ai

Underwriter

An underwriter who wishes to check consistency of a loan decision by comparing it against past decisions
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Risk Manager

A risk manager who wishes to investigate anomalies and disparities of the past decisions
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Researcher

A researcher who wants to understand the mortgage industry characteristics completely
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Borrower

A consumer who wants to make an informed decision about choosing a mortgage lender
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the year of foundation
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solutions delivered
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professionals and growing
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Key Concepts

Disparity of Decisions

Measure odds groups face in a specific outcome. Evaluate disparity within parameters. In a lender example, age groups compared for consistency.

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Decision Factors

FairBorrow™.Ai complies with Fair Lending rules, identifying only permissible factors like Income, Property Value, Loan Type in its analysis.

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Decision Anomaly

Decision anomalies occur when similar loans yield different outcomes. We identify them, aiding risk managers in enhancing processes.

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Disparity Factors

FairBorrow™.Ai uses prohibited factors like Age, Gender, Race for Disparity Analysis, measuring decision differences within each factor for fairness assessment.

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Use Cases

Practical Applications of Disparity Analysis

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

Home Lending (Mortgage) Decision Process

FairBorrow™.Ai addresses mortgage industry challenges with regulatory compliance, data solutions, and infrastructure options.
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Credit Decision Processes - Auto Finance, Credit Cards

Improve credit decisions with FairBorrow™.Ai using custom solutions in confidential, secure collaboration with financial institutions.
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Anti-Money Laundering and Bank Secrecy Act

We improve AML/BSA processes, offering insights and leveraging decision anomalies for improvements in institutions.
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Optimizing Stock Trading Decision-Making

Stock trading compliance, Securities and Exchange Commission(SEC) insights, and anomalies for consistent, improved financial processes.
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Insurance Policy Underwriting Decisions

FairBorrow™.Ai enhances insurance underwriting, providing insights and leveraging decision anomalies for process improvements.
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Insurance Claims Adjudication Decisions

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

What they say

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Headquarter

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

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Copyright © 2024 FairBorrow AI, All rights reserved.

Headquarter

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

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Copyright © 2024FairBorrow™.Ai | All rights reserved.

We have implemented using publicly available HMDA data

Underwriter for Mortgage Lending

Our proprietary, AI-based process enables decision- makers to test their decisions for consistency within their company, region, by product or any other accepted underwriting factor. FairBorrow™.Ai leverages its proprietary AI-based Data Management processes and produces insightful summaries automatically.

FairBorrow™.Ai is not a decisioning model. We do not recommend reversing or changing any loan decisions made on the basis of consistency checks. The purpose of FairBorrow™.Ai is to help identify improvements in the decision-making process itself by understanding the causes of decision inconsistencies. The outcome of an inconsistency check does not imply a right or wrong decision, or any other wrongdoing.

FairBorrow™.Ai does not impose any of its own restrictions or biases in this process and leverages various key risk indictors from within the decision data set to arrive at the insightful conclusion whether the specific decision(s) being checked are consistent or not.

Decision-makers will follow their institution’s policies and procedures to make their decisions, and then check the consistency of their decisions. Such checks inform the decision-makers about opportunities in streamlining their processes.

Our proprietary AI-based process enables decision-makers to test their decisions for consistency within their company, region, by product or any other accepted underwriting factor. FairBorrow™.Ai leverages its proprietary AI-based Data Management processes and produces insightful summaries automatically.

FairBorrow™.Ai is not a decisioning model. We do not recommend reversing or changing any loan decisions made on the basis of consistency checks. The purpose of FairBorrow™.Ai is to help identify improvements in the decision-making process itself by understanding the causes of decision inconsistencies. The outcome of an inconsistency check does not imply a right or wrong decision, or any other wrongdoing.

FairBorrow™.Ai does not impose any of its own restrictions or biases in this process and leverages various key risk indicators from within the decision data set to arrive at the insightful conclusion whether the specific decision(s) being checked are consistent or not.

Decision-makers will follow their institution’s policies and procedures to make their decisions, and then check the consistency of their decisions. Such checks inform the decision-makers about opportunities in streamlining their processes.

We have implemented using publicly available HMDA data

Risk Manager

A risk manager could be a quality assurance professional, underwriting team manager, or compliance professional including a Fair Lending compliance officer). A risk manager would normally review past decisions for inconsistencies and anomalies to identify potential enhancements in the decisioning process.

In identifying anomalies, FairBorrow™.Ai leverages disparity in decision rates as one of the key risk indicators. Use of Data Driven Artificial Intelligence, with no external assumptions and bias imposed allows a risk manager to quickly identify groups of loans with highest decision anomaly, based on product, geography and any of the factors used in the decisioning process. The presence of anomalies in past decisions does not necessarily indicate any wrong decisions and any other wrongdoing.
A risk manager can leverage such lists of anomalies and AI driven comparative loans to determine what process enhancement would create a more consistent decision process. Increased consistency in decisions

Higher levels of consistency in decision making reduces any potential or perceived bias and produces results that are more closely aligned with business objectives.

A risk manager could be a quality assurance professional, underwriting team manager, or compliance professional, including a Fair Lending  compliance officer). A risk manager would normally review past decisions for inconsistencies and anomalies to identify potential enhancements in the decisioning process.

In identifying anomalies, FairBorrow™.Ai leverages the disparity in decision rates as one of the key risk indicators. Use of Data-Driven Artificial Intelligence (AI), with no external assumptions and bias imposed, allows a risk manager to quickly identify groups of loans with the highest decision anomaly, based on product, geography, and any of the factors used in the decision process. (Note, however, that the presence of anomalies in past decisions does not necessarily indicate any wrong decisions and any other wrongdoing.)

A risk manager can leverage such lists of anomalies and AI-driven comparative loans to determine what process enhancement would create a more consistent decision process.

Higher levels of consistency in decision-making reduce any potential or perceived bias and produce results that are more closely aligned with business objectives.

We have implemented using publicly available HMDA data

Researcher

A researcher could be anyone who would like to gain deep AI driven insights into data, which contains charts, summary level information and automatically produced narratives. The researcher neither needs to fully understand the intricacies of the data they are researching, nor do they need to be concerned with Data Science.

Our proprietary Data Management Processes allow simple chat interface, to guide the researcher into the best choices possible for these deep insights. Chat interactions are kept strictly confidential and rea used for improving the experience.

FairBorrow™.Ai produces deep insights into data and does not impose its own opinions or judgment. We do not recommend for or against any lender or Financial Institution, and the presence of any clickable links in our reports that take you to a Financial Institution’s web site is not a sign of our endorsement. In some cases, we may collect a referral compensation from such a Financial Institution if you do click on their link. Please see our Privacy policy for further details.

A researcher could be anyone who would like to gain deep AI-driven insights into data, which contains charts, summary level information, and automatically produced narratives. The researcher does not need to fully understand the intricacies of the data they are researching, nor do they need to be concerned with Data Science.

Our proprietary Data Management Processes allow simple chat interface, which guide the researcher into the best choices possible for these deep insights. Chat interactions are kept strictly confidential and are used for improving the experience.

We have implemented using publicly available HMDA data

Potential Mortgage Borrower

FairBorrow™.Ai is neither a mortgage provider nor a financial institution of any kind. Any information collected by us does not constitute a mortgage application, just as any responses from the FairBorrow™.Ai system to your queries do not guarantee a loan approval, denial, or any specific outcomes such as interest rates.

FairBorrow™.Ai uses its proprietary Data Management Processes and HMDA data to produce deep and easy-to-understand insights for potential borrowers so that they can make informed decisions.

We do not recommend or critique any lender or financial institution. The presence of any clickable links in our reports, which may take you to a financial institution’s or other lender’s web site, is not an endorsement of that financial institution or other lender. In some cases, we may collect referral compensation from such a financial institution if you do click on their link. Please see our Privacy policy for further details.

Disparity of Decisions

Disparity is a measure that shows the odds a group of applicants face when compared to another group, based on a specific outcome. For it to be meaningful, care must be taken to measure disparity within applicable parameters. In the lender-oriented example below, two age groups are compared based on the decision status “Originated” as defined within the HMDA Schema.

GRP Applicant Age Group Population Originated Origination Ratio Disparity
GRP 1
25-34
123,961
47,792
47792/123961 = 38.55%
16.44%
GRP 2
45-54
211,699
97,688
97688/211699 = 46.14%
0%

The presence of disparity does not imply a shortcoming or defect in the decision-making process or model. It is simply one of the measures for consistency of decisions. Understanding how much and where disparity exists in the data produces actionable intelligence for process enhancements.

Decision Factors

The Fair Lending Rules forbid the use of prohibited factors in decision-making. FairBorrow™.Ai leverages only such factors that are permissible for credit decision-making in its analysis. Such factors include income, property value, type of loan, type of product, etc.

Decision Anomaly

A “decision anomaly” is an instance where two similar loans receive different outcomes, such as one being originated, and the other being denied. The presence of a “decision anomaly” does not imply a shortcoming or defect in the decision-making process or model and is simply derived from the presence of a disparity in decisions. The proprietary Data Management Processes at FairBorrow™.Ai can identify decision anomalies from various angles to enable a risk manager to quickly identify potential actions for process enhancement.

Disparity Factors

Fair Lending-prohibited factors are used as Disparity Factors by FairBorrow™.Ai. These include Age, Gender, Race, Ethnicity, etc. Disparity of decisions is measured within each of these disparity factors.

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.