Financial Services Archives

Large Australian bank identifies the root cause of customer DSAT and enhances self-service experience using GenAI on AWS

Business challenge

A large Australian bank found itself overwhelmed by the volume of customer interactions handled by their contact centers every month. Thousands of calls and chats were happening, but the bank lacked the right tools to effectively analyze this data and extract valuable insights.

Here’s what hampered their efforts:

Inaccurate transcription: The bank’s existing contact center software struggled with accurate speech-to-text conversion, leading to a distorted picture of customer conversations.
Limited NLP capabilities: Their natural language processing (NLP) technology relied heavily on topic modeling, a basic approach that couldn’t identify deeper nuances within customer interactions.
Manual tagging bottleneck: The process of tagging customer issues relied on manual work by agents, which was time-consuming, prone to errors, and limited the effectiveness of data analysis.

Implementation approach

Fractal leveraged Customer Interaction Insights (CII), a GenAI-powered solution, to analyze voice calls and agent chats and draw actionable insights such as the root cause of customer dissatisfaction, sentiment, and other patterns.

Using CII within the bank’s AWS ecosystem, we harnessed the power of Amazon EKS, Amazon Bedrock, and Amazon OpenSearch Service to distill actionable insights from voice calls and agent chats. With features like auto-transcription using STT and unsupervised intent and entity extraction, CII unveiled hidden patterns and pain points, empowering the bank to make data-driven decisions for improvement.

CII offered a range of features designed to transform the bank’s approach to customer data:

Auto-transcription using STT: CII’s proprietary speech-to-text (STT) feature delivered higher accuracy, providing a clear and reliable foundation for further analysis.
Unsupervised intent and entity extraction: Unlike traditional NLP, CII’s unsupervised approach could automatically identify customer intent and key entities within conversations, uncovering hidden patterns and pain points.
Unified dashboard for actionable insights: A central dashboard brought together all the extracted information, presenting emerging trends and actionable insights at a glance. This empowered the bank to make data-driven decisions for customer experience improvement.

Impact delivered

Achieved 85% accuracy on call-driver identification

Built a feedback loop to train agents

Improved STT accuracy from 60% to 85%

Customer Interaction Insights solution is now available on the AWS Marketplace; click here to learn more.

Global accounting firm leverages FractalGPT to automate employee support and improve compliance across multiple internal processes

Summary

FractalGPT is a generative AI-powered platform that was customized and deployed to provide employees access to internal knowledge around compliance, HR policies, and IT through an intuitive and engaging natural language chat interface.

The virtual assistant was trained on hundreds of internal knowledge documents and integrated with the firm’s enterprise IT tools. It uses a Large Language Model (LLM) to perform semantic search and response generation, delivering accurate and coherent answers to employee questions.

The bot handles over 10,000 questions per month with an average accuracy of 93%, saving time and money for the firm and improving employee satisfaction and compliance.

Business challenge

For a global accounting firm with over 13,000 employees across seven locations, managing internal knowledge and answering employee questions around compliance, HR policies and IT was a daunting challenge.

As it’s often the case with traditional tools, existing knowledge tools were unable to provide precise and actionable answers to employee’s questions.

Therefore, the firm wanted to provide its employees with a more convenient and reliable way to access relevant information and perform day-to-day transactions without burdening the HR teams with repetitive requests.

The firm also wanted to improve employee satisfaction and compliance by offering 24×7 multilingual, region-specific support and a unified interface for common actions for both finance and HR, like job expense reimbursement and vacation applications.

Approach

To achieve this goal, the firm partnered with Fractal, a leading provider of B2B AI solutions. In eight weeks, Fractal implemented and customized its Generative AI-Powered FractalGPT solution, a state-of-the-art chat platform that can understand natural language queries and generate accurate and coherent responses based on existing knowledge sources.

FractalGPT was deployed on the firm’s intranet, where it was integrated with the human resources management system (HRMS) and several other backend tools. The virtual assistant was further customized by training it on thousands of documents covering various topics such as handbooks, compliance guidelines, policy documents, and more. It leveraged a large language model (LLM) to perform semantic search and response generation, ensuring that the answers were relevant and consistent with the source documents. The approach ensured that no confidential or company-specific information left the company tenant.

Furthermore, questions and answers are filtered and logged for later analysis, insights, and decision-making.

Finally, FractalGPT also provides role-based access control by ensuring that the solution only shows authorized information to the end user.

Architecture

Demo

In this video demo, you’ll see how FractalGPT can be used to provide quick and correct responses to employees’ questions on internal expense policies using natural language.

Results

FractalGPT became the go-to solution for employees who needed quick and easy access to internal knowledge.

The solution handles over 10,000 questions per month from employees across different functions and locations and the adoption is increasing 10% month over month.

FractalGPT achieves an average accuracy of 93%, which meant that over 9,000 direct human interactions were avoided per month.

This resulted in an estimated saving of about $1 M per year, as well as increased employee satisfaction scores through faster and round-the-clock support.

The solution also improved compliance for the organization by providing accurate and up-to-date information on policies.

Read this customer story on LinkedIn.

Deliver next best product recommendations during customer interactions

The Big Picture:

A leading retail bank was facing low customer engagement and satisfaction with its customers. The existing analytical models on product propensities generated lower accuracy, and missed critical data elements, such as offline and online interactions and transactions, and prevented an objective arbitration of offers among multiple competing product offers. This resulted in sub-optimal customer experience and lower response rates.

Transformative Solution:

To address the company’s challenges, a new next best product and service recommender was built using deep learning. It was designed to predict the top three recommendations from among a wide suite of products, and for services.

A single customer view was prepared with 4,000+ attributes such as customer product holdings, transactions, in-bank transactions, and online interactions.

The models were tested on a select population within the lead scoring platform and deployed centrally.

The Change:

As a result of the engagement, customer product-offtake rates jumped by 60%, resulting in significantly higher marketing ROI.

A leading private bank leverages behavioral sciences to improve delinquency resolution

The Big Picture

Delinquency is not only an unfamiliar situation for credit consumers but also one which is most often accompanied by temporary/permanent financial difficulty. Therefore, consumers display very little ability to cope with it. In this context, consumers display an ‘avoidance’ tendency – making themselves difficult to reach and making promises that they can’t keep. This leads to significant operations costs, write-offs, and frustration amongst service agents.

Transformative Solution

Behavioral Sciences principle of ‘Commitment’ was deployed in the agent-customer conversation script to strengthen consumers intent to pay the dues one he has made a ‘promise to pay’.
Behavioral Sciences principle of ‘Loss Aversion’ and ‘Reciprocity’ was brought into play to ensure that consumers prioritize delinquency resolution over other financial commitments.
The scripts were customized to different delinquent customer segments – fresh tickets, 90 days and 180 days delinquent customers.

The solution was deployed across the bank’s multiple call centers throughout the country with minimal training requirement.

The Change

The scripting changes were intuitively appealing to the agents as they received a favorable response from customers. The subtleness of the solutions ensured that the company was able to scale the solution across multiple centers in the country. The key results of this deployment were:

A 40% reduction in the number of connects with delinquent consumers before a resolution is achieved
A 20% reduction in unresolved delinquencies

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