Convergence

Credit dispute algorithms routing with raw bureau data

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Paradigm

Distinct concept or thought pattern defining a particular period

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Determinism

Cause-and-effect; when a applicable paradigm exists

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Calculability

Effective method for solving a problem for a specific class

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Creditxp Software as a Service

Data-driven knowledge based software algorithms resolving complex consumer credit report issues.


Characteristics of the Software

Convergence

The degree that a function or sequence approaches as the input considers the relevant values:

The analysis of data imported from a consumers credit report with predetermined algorithms. Algorithms signify potential data furnisher errors, inaccuracies, inconsistencies, or outdated information.

Paradigm

The theory of knowledge is a distinct concept or thought pattern defining a particular period:

The assessment of the analysis of the data imported from the consumers credit report to target solutions in attaining accurate data reporting from data furninshers.

Determinism

Effective method for solving a problem for a specific class after a finite number of steps:

Results of a performed investigation; determined by prior conditions identifying historical denoted existence of data reported by a data furnisher after the acknowledgment of a particular request.

Calculability

Cause-and-effect; when a given paradigm exists depending upon conditions determining the event:

Possibilities rather than exact answers; methods with likely electable results needed to produce solutions to generate success in settling unfavorable conclusion of investigations.

A collaboration engaging lenders, realtors and builders. Offering a system combined with support is invaluable when working with everyone involved to update your clients progress. You will form a unified relationship beyond measures providing an astonishing service that keeps everyone informed. No other system on the market gives you the ability to offer such a remarkable product and service.

Handle collections that were not resolved with sanitizing the file. When collections have been validated as accurate you now have options to resolve. Help them Settle it or file a Complaint to the CFPB for possible FDCPA Violation. This service is extremely valuable due to collections can be very complex. One of the feature is that the system creates letters to negotiate the collection for less than the amount owed. The system provides the tools needed to settle these accounts.


Software Case Study

X1 Analysis

Tri-Merge Summary with Action Plan:

  • Average starting FICO score across all 3 bureaus: 567
  • Percent with insufficient trade-lines that need to create credit: 22%

Based on originations in 1Q'14 where the disputes process was executed at least once.

X2 Disputes

Automated Validations and Error Corrections:

  • Average number of accounts deleted: 7
  • Average FICO score improvement after 45 days: 36
  • Average number of settlements remaining: 1.5
  • Percentage of accounts with no settlement letters: 48%

X3 Settlements

Side-by-Side Results and Settlement Letters:

  • Average FICO score improvement after 90 days: 61
  • Percentage of accounts removed from settlement: 62%
  • National Average FICO Score: 639
  • National Average FICO Score: 639

Revenue StudyFundable new organic growth from existing applications:

Funded Loans $2,000,000,000
Est. # of loans declined due to credit report 19,600 Industry averages: Size of loan $168,000;
56% declined due to bad credit report
Est. # of these approved after processing 4900 Estimate 25%. Based on modeling and validated with broker survey results.
Est. revenues generated from additional loans $34,800,000 Estimates (per loan): BPS 1.90%, Fee Income $1000, Servicing Fees 0.25%, “Extra Services” premium BPS 0.5%

The full spreadsheet model is available on the X tab allowing you to plug in your own numbers to calculate your own ROI.

Organic Growth Analysis Case Study

Target Market:

Industry wide, 56% of all mortgage applicantions are declined. Using a company with $2B in annual lending, this adds up to about a 20,000 lose from obtainable existing applications.

With credit scores declining and trying economic times, lenders must learn to tap this market to stay competitive and to grow their business organically.

For a functional analysis spreadsheet to calculate your own numbers feel free to contact us.

Analysis 1

Analysis based on $1,800,000,000 funded portfolio adapting an average loan amount of $168,000 to conclude the analysis. Funding 44% of the portfolio yields 10,714 individual loans with in the portfolio.

To configure the ROI of success from the software we must first establish the revenue for the existing portfolio. Using 2.000% BPS, 0.250% SRP and $1,000 for loan fees the revenue for the portfolio would be approximately $51,214,289.

With this established conclusion we can now determine new organic growth potential revenue from the 56% of loan applications that were declined due to credit scores.

Percentages of declined loans
Percent of Loans that are not approved 66%
Due to Credit Scores 56% Percentage of applications that do not qualify because of Credit Score
Due to Appraisal, DTI, Loan Type, etc 10%
CURRENT REVENUE MODEL
Current revenue per loan $3,360 Conclusion from the portfolio figures
Current revenue per year $51,214,286 Using the analysis from the portfolio figures
Current number of funded loans 10,714

Spreadsheet available to calculate your own figures.

Analysis 2

Analysis based on $1,800,000,000 funded portfolio adapting an average loan amount of $168,000 to conclude the analysis. Funding 44% of the portfolio yields 10,714 individual loans with in the portfolio.

To configure the ROI of success from the software we must first establish the revenue for the existing portfolio. Using 2.000% BPS, 0.250% SRP and $1,000 for loan fees the revenue for the portfolio would be approximately $51,214,289.

With this established conclusion we can now determine new organic growth potential revenue from the 56% of loan applications that were declined due to credit scores.

New leads from declined loan files
Nubmer of declined applications due to Credit Score 17,647 New leads per year from exsiting portfolio
Success Rate 25% Percentage of new leads converted to new loans.
Number of new loans 4,412
NEW REVENUE MODEL
New revenue per loan $4,750 concluded from Analysis 1 (includes $840 premium)
Premium per new loan $840 0.500%
New revenue from restructured lead $24,794,118 Using the table from Analysis 1
Percent increase from organic growth 48% Using the 25% success rate capturing the declined loans

Spreadsheet available to calculate your own figures.

Analysis 3

Analysis based on $1,800,000,000 funded portfolio adapting an average loan amount of $168,000 to conclude the analysis. Funding 44% of the portfolio yields 10,714 individual loans with in the portfolio.

To configure the ROI of success from the software we must first establish the revenue for the existing portfolio. Using 2.000% BPS, 0.250% SRP and $1,000 for loan fees the revenue for the portfolio would be approximately $51,214,289.

With this established conclusion we can now determine new organic growth potential revenue from the 56% of loan applications that were declined due to credit scores.

Lead generation savings
Estimated cost per internet lead $20
Savings per year by using internal lead $352,941 Using loan costs from Analysis 1
Number of new loans 4,412

Spreadsheet available to calculate your own figures.