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Tel: 610-564-1000 

1108-1110 Chestnut Street

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Philadelphia PA, 19107

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Servicer achieves record-breaking

results with

AI Insurance Tracking

CASE STUDY 1

The problem

For the past 15 years, this nationwide Loan Processing Servicer has worked tirelessly to deliver high quality loan processing at scale. Up until 2017, their team relied heavily on around-the-clock manual labor,  to get millions of documents reviewed and processed every quarter.
 

Since it took about 48 hours to review the documents, while some documents were still in the queue, other team members made additional outbound calls to the borrower for more insurance proof. This flow generated 18,000 additional outbound requests resulting in over 100,000 more documents to review each year.

In hopes of streamlining the process and reducing redundant work, this servicer reached out to HeavyWater to see if artificial intelligence was the answer to their needs.

Problem

Long processing cycle

for proof of insurance  causes redundant borrower requests and high costs to the servicer

 Solution

An artificially intelligent assistant that processes insurance documents at scale.

Outcome

Quicker turn-around, increased visibility and lowered costs.

The Solution

Working alongside operators, AIVA (an artificially intelligent virtual assistant) reviewed hundreds of thousands of documents to be able to review insurance, draw conclusions and increase turn-around speed. 
 

The Results

HOURS

48 hours reduced to 1hour 

The servicer's average turnaround time per insurance document historically took 48 hours to review and approve. After AIVA reviewed the documents first the reviewer could pass through the document in an hour.

OPERATORS

Workload of 120 operators reduced to 10 operators

AIVA increased through-put such that 10 operators could do the same volume as 120 operators in a fixed amount of time. 

CASE STUDY 2

Lender saves thousands of  hours by employing an

AI Assistant to help Verify Borrower Assets

Problem

Long processing cycle

for proof of insurance  causes redundant borrower requests and high costs to the servicer

 Solution

An artificially intelligent assistant that processes insurance documents at scale.

Outcome

Quicker turn-around, increased visibility and lowered costs.

The problem

For the past 50 years, this nationwide lender committed themselves to quality loan processing at scale. Up until 2018, they relied on around-the-clock manual labor,  to review millions of asset statements each year.
 

Asset reviews of bank statements typically took their teams around 30 minutes each time someone evaluated a borrower's loan. Then this same review would happen at least three more times in the the lifecycle of a loan--including a review by the loan processor,underwriter and quality control representative. 

In hopes of reducing time bet, increasing the speed of asset review and reducing redundant work, this lender agreed to enlist HeavyWater to see if artificial intelligence was the answer to their needs.

Problem

Manual asset review often results in sifting through  batches of statements and long intervals of reviewing borrower information

 Solution

An artificially intelligent assistant  that reads organizes & summarizes asset statements at scale.

Outcome

Quicker turn-around, increased traceability and lowered costs.

The Solution

Working alongside operators, AIVA (an artificially intelligent virtual assistant) reviewed hundreds of thousands of bank statements to be able to r draw conclusions and increase turn-around speed of asset verification. 
 

The Results

FEEDBACK

Feedback 3-5 days reduced to 1 hour

When lenders originate loans they typically go back and forth with borrowers on average 3 times to gather the right assets to complete the borrower asset profile. Now with borrower summary data, processors can see right away what borrowers need to provide to complete their review.

WORK TIME

Cumulative Review Time

 95 minutes reduced to 15 minutes

AIVA increased through-put and traceability of borrower assets via her summary user interface. Each time a reviewer looks at borrower details they can drill down and track the threads of data back to their point of origin--resulting in time and monetary savings.