Case study · Data Annotation

Simplifying Compliance Risk Monitoring Through Text Annotation

One of the largest lending companies modernized compliance risk monitoring with Fusion CX, automating QA through speech-to-text and REGEX annotation to reach 90% audit accuracy and a 35% NPS increase.
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Compliance analyst reviewing annotated call transcripts and speech waveforms for a lending company
90%
accuracy in quality audits
Client
Large consumer lending company
Industry
Data Annotation · Lending compliance
Services
Text annotation, QA automation, speech analytics and data science
Delivery
The results

Full QA coverage, with accuracy to match.

Measured outcomes from the automated compliance monitoring program.
90% | accuracy in quality audits 35% | increase in net promoter score 100% | automated QA 30k+ | operational hours every month 21 | call types with scoring models
01Challenge

Manual monitoring left compliance risk hidden in the calls.

One of the largest lending companies, focused on the long-term financial success of everyday Americans, relied on a manual approach to compliance risk monitoring. Listening to only 4% of monthly calls was slow and error-prone, and random audits gave an incomplete picture of service health.

Limited call coverage

Only 4% of monthly calls were reviewed.

Skewed governance

Random audits could not accurately gauge service health.

Missing correlation

Weak link between CSAT and QA scores obscured performance.

Inadequate technology

No user-friendly speech analytics, and REGEX was hard to manage.

02Solution

Automated, annotation-driven QA across 21 call types.

Fusion CX built a plan to transform the process, scaling operational capacity to over 30,000 hours a month from October 2021. Speech-to-text was integrated with third-party technologies for call recordings, and REGEX experts broke the customer experience into 21 call types with clear success criteria for automated audits.

Capacity enhancement

Over 30,000 operational hours a month from October 2021.

Speech-to-text QA

Call recordings integrated with third-party technologies.

REGEX expertise

21 call types with defined audit success criteria.

Data science and scoring

Annotation modeling, Tableau visuals and specialized scoring models.

03Impact

Accurate audits, a higher NPS and leaner QA operations.

The program reached 90% accuracy in quality audits, which in turn drove a 35% increase in Net Promoter Score. With QA 100% automated, the client optimized resource utilization and reduced manpower costs.

Scoring models built on qualified annotations across 21 call types, visualized in Tableau, gave the team a structured view of compliance performance.

When every call can be scored, compliance no longer depends on the luck of the sample.

04Key insights

What other programs can take from this.

  • Sampling a small share of calls leaves compliance risk and service health hard to see.
  • Breaking the customer experience into defined call types makes audit automation precise and measurable.
  • Accurate automated QA can lift customer loyalty, as a 35% NPS increase showed.
Download the full case study

In this case study, you’ll see:

The challenges behind manual, sample-based compliance risk monitoring
How speech-to-text integration, REGEX annotation across 21 call types and Tableau-driven scoring models automated QA
The measurable outcomes: 90% audit accuracy, a 35% NPS increase, 100% automated QA and 30,000+ operational hours a month
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Lending Company
Case study · Data Annotation

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