Prodigal Technologies Inc. Case Studies Unifin Employs Prodigal’s Natural Language Engine for Improved Customer Service

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	Unifin Employs Prodigal’s Natural Language Engine for Improved Customer Service Prodigal Technologies Inc.

Unifin Employs Prodigal’s Natural Language Engine for Improved Customer Service

Prodigal Technologies Inc.

Technology Category	Analytics & Modeling - Natural Language Processing (NLP) Analytics & Modeling - Real Time Analytics
Applicable Industries	Finance & Insurance
Applicable Functions	Human Resources Sales & Marketing
Use Cases	Speech Recognition
Services	Data Science Services
Challenge	The coronavirus pandemic led to a sudden loss of jobs for approximately 15 million Americans. This situation led to a surge in stressful discussions between Unifin's agents and borrowers, particularly concerning loss of employment and health concerns among the elderly. Unifin identified a problem where some agents failed to empathize with the borrowers' stressful situations, which was considered a Level 4 infraction. Unifin needed a quick mechanism to identify these infractions among thousands of calls each day and educate the agents to drive more empathy towards borrowers. Read More
About Customer	Unifin is a company that provides support, assistance, and education to borrowers throughout the loan process. The company prides itself on its highly trained agents who engage and collaborate with borrowers over the phone. Unifin is committed to maintaining a high level of empathy towards borrowers, particularly those affected by the pandemic. The company has established a moral code of conduct for its agents to follow during such calls, and any violation of this code is taken seriously. Read More
Solution	Prodigal’s Natural Language Engine was employed to identify stressful discussions related to loss of employment and health concerns among the elderly. The engine, with its expertise in the collections domain, was able to surface insights without specifically being asked to search for a list of keywords. It flagged all conversations of this nature and made the trends prominently visible on its interface. This made it easy to identify agents who were repeat offenders or needed coaching. Prodigal’s smart reports were shared with all clients for free within two weeks of the shutdown being enforced. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Natural Language Processing (NLP)

Analytics & Modeling - Real Time Analytics

Applicable Industries

Finance & Insurance

Applicable Functions

Human Resources

Sales & Marketing

Use Cases

Speech Recognition

Services

Data Science Services

Challenge

The coronavirus pandemic led to a sudden loss of jobs for approximately 15 million Americans. This situation led to a surge in stressful discussions between Unifin's agents and borrowers, particularly concerning loss of employment and health concerns among the elderly. Unifin identified a problem where some agents failed to empathize with the borrowers' stressful situations, which was considered a Level 4 infraction. Unifin needed a quick mechanism to identify these infractions among thousands of calls each day and educate the agents to drive more empathy towards borrowers.

About Customer

Unifin is a company that provides support, assistance, and education to borrowers throughout the loan process. The company prides itself on its highly trained agents who engage and collaborate with borrowers over the phone. Unifin is committed to maintaining a high level of empathy towards borrowers, particularly those affected by the pandemic. The company has established a moral code of conduct for its agents to follow during such calls, and any violation of this code is taken seriously.

Solution

Prodigal’s Natural Language Engine was employed to identify stressful discussions related to loss of employment and health concerns among the elderly. The engine, with its expertise in the collections domain, was able to surface insights without specifically being asked to search for a list of keywords. It flagged all conversations of this nature and made the trends prominently visible on its interface. This made it easy to identify agents who were repeat offenders or needed coaching. Prodigal’s smart reports were shared with all clients for free within two weeks of the shutdown being enforced.

Impact #1	Unifin was able to swiftly incentivize recommended behaviors among its agents.
Impact #2	The company identified a dozen calls where the agents had shown great consideration and empathy with the borrower.
Impact #3	Prodigal enabled Unifin to identify certain agents who had committed severe infractions and these agents were guided to improve.

Impact #1

Unifin was able to swiftly incentivize recommended behaviors among its agents.

Impact #2

The company identified a dozen calls where the agents had shown great consideration and empathy with the borrower.

Impact #3

Prodigal enabled Unifin to identify certain agents who had committed severe infractions and these agents were guided to improve.

Benefit #1	98%+ Correct Call Tags
Benefit #2	24 hrs Implemented in a day
Benefit #3	40+ ARM domain tags

Benefit #1

98%+ Correct Call Tags

Benefit #2

24 hrs Implemented in a day

Benefit #3

40+ ARM domain tags

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Overview

Unifin Employs Prodigal’s Natural Language Engine for Improved Customer Service

Operational Impact

Quantitative Benefit