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Reimagining financial services CX with contact center AI

Reimagining financial services CX with contact center AI

How can financial institutions harness the power of deep data and insights and in turn, create impactful programs around agent empowerment and empathy?

In the face of the COVID-19 pandemic, digital transformation across the financial services industry has accelerated at an unstoppable rate. With customer service agents shifting to working from home overnight, heavy surges in call volume, and stringent compliance requirements, banks, lending agencies, and debt settlement companies are facing a new, never before seen set of challenges. 

Dealing with these challenges and focusing on delivering a world-class customer experience rests in the insights. But financial institutions today are falling short in leveraging data to drive differentiated customer experiences. According to the Digital Banking report, 60% of financial institution executives stated that the quality of data used by business intelligence was either unacceptable (22%) or acceptable, but requiring significant additional support (38%). 

So, how can financial institutions harness the power of deep data and insights and in turn, create impactful programs around agent empowerment and empathy?

That’s where new technologies like contact center AI come into play, bringing the power of AI, machine learning (ML), and Natural Language Processing (NLP) to streamline operations and improve agent performance.

In this blog we'll show how these technologies are changing the game for financial services. We'll cover:

  • How to analyze and improve your CX strategy with AI
  • How to monitor compliance, automate fraud detection, and improve quality assurance programs
  • How to coach agents into top performers

Ripe for disruption: 3 ways to reimagine financial services contact center CX

1. Analyze your customer experience strategy

Conventional financial organizations are doubling down on Contact Center AI to fuel their deep understanding of customers.

A leading national bank using Observe.AI serves high profile clientele from celebrities to athletes. With such elite accounts to manage, delivering a brilliant customer experience is always their #1 priority.

The problem

While 90% of their customers needed agent assistance, only 35% were really satisfied with their services. To overcome this hurdle, their team first had to precisely understand what points of customer frustration, and then fix it.

The solution

Here’s what happened when they integrated Contact Center AI.

  • They were able to monitor and analyze 100% of their support calls (up from ~0.002%)
  • Then using AI, they searched through thousands of calls to find points of customer frustration
  • With almost ~90% transcription accuracy, the QA process was 5x faster
  • Leadership now had complete visibility into anti-money laundering (AML) compliance, authentication, and disclosures, lawsuit mention, security breach, mentions of redacted entities like SSN, card details, PII, PCI, and more
Contact center AI enables financial institutions to hone in on the interactions that impact CX most, like supervisor escalations.

Using these insights, the bank could build a data-driven and top-notch customer experience strategy.

2. Automate your QA processes & agent evaluations

To achieve extreme customer centricity, it’s important to enable efficient quality assurance processes, coaching agents to become your top brand representatives.

But with thousands of calls recorded daily, quality assurance on every call can become a labor-intensive, manual and costly line of business.

For example, a call center with 50 agents handling 5000 calls per day can save $2500 USD each day by simply shortening the Average Handle Time (AHT) by just 30 seconds. 

To put this in context, one of our customers, Root Insurance, evaluates close to 102,000 calls per month. They were evaluating less than ~0.003% of these calls which led to a myriad of problems:

  1. Inefficient and costly QA process
  2. Agent calls were cherry-picked and produced unfair evaluations 
  3. Lack of visibility in every call for dead-air, hold-time, compliance issues, etc.
AI-driven QA evaluation forms bring the entire call evaluation process to a single interface, backed by context.

With contact center AI, Root transcribes millions of call recordings, detects keywords in accurate transcripts, highlights issues like dead-air, average hold time, compliance, and PPI information redaction in every call. 

Results included:

  • Quality checks on 100% voice calls 
  • 37% increase in adoption of mandatory compliance dialogues
  • 150 agents moved to remote working overnight
  • Decreased call evaluation time by 87% and helped each analyst check 5x more calls

By leveraging AI for quality assurance automation, financial institutions can build highly efficient, time-saving and cost effective processes.

3. Coach and Upskill the Contact Center 

Leveraging speech analytics and AI to offer targeted, data-driven coaching is one use case financial companies are doubling down on. It all starts with improving trust and transparency around how the team’s efforts are impacting the overall CX. 


Here’s an example of a leading lending company struggling with agent coaching programs. The company depended on their agents to exhibit qualities of trust, transparency and rapport to convert customers in search of a loan. However, in coaching these agents, the company faced two distinct challenges:

  • Subjective reporting on agent empathy and customer sentiment meant the company didn’t have accurate and objective context to coach on 
  • Since QA teams were randomly analyzing a small sample of calls, coaching teams lacked visibility into the most important, impactful areas to cover.

To gain visibility into agent performance and drive better coaching programs, the company relies on phrase monitoring and sentiment analysis delivered via contact center AI. This lets them not just see what agents are saying, but how they’re saying it.

Phrase analysis analyzes the transcribed call for phrases and reports on how often they are used. For sentiment analysis, tonality-based sentiment detection gauges the tone of the agent and analyzes how it was said.

The result? Every agent is then scored on the positive and negative moments used in the phrases. 

The supervisors and agents at the company could now see leaderboards and detailed coaching reports highlighting tone, phrases, dead-air, AHT and overall call quality. With such immense visibility into an agents’ top and bottom areas of opportunities, coaching got faster, effective and more personalized.

The impact?

They saw a 39% increase in display of empathy on call.


It doesn’t end at agent performance or quality assurance. It also frees up time for leaders to pursue more creativity and think more strategically across their department’s functions, as well as how they set and measure common objectives.

As a leader, whether you are looking to add value to your contact centers or reimagine it, AI is the true way forward.

About the Author

sharath observeai

Sharath Keshavnarayana is the Co-founder and CRO at Observe.AI, and has over a decade of experience in the customer care space. Connect with Sharath on LinkedIn.

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Sharath Keshava
Co-Founder & CRO, Observe.AI
LinkedIn profile
November 24, 2020

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