Banks receive millions of complaints each year from customers, employees, and regulators. Handling complaints effectively and efficiently is essential to maintain customer satisfaction and protect the bank’s reputation.

Banks typically respond to complaints by:

  • Investigating the complaint to determine the facts and identify the root cause.
  • Communicating with the customer to keep them informed of the investigation’s status and to resolve the complaint.
  • Taking corrective action to prevent the problem from happening again.
  • Providing compensation if it finds that the customer was harmed by the problem.

Historically, agents of the bank have handled complaints. While manually responding to complaints can offer a human touch, it is time-consuming and labor-intensive. Bank agents may also struggle to track the status of complaints and ensure that they are resolved in a timely manner.

AI is accelerating complaint resolution for banks

AI can help banks automate many of the tasks involved in complaint handling, such as:

  • Identifying, categorizing, and prioritizing complaints.
  • Assigning complaints to staff.
  • Tracking the status of complaints.
  • Generating reports on complaint trends.

This leads to faster responses, better customer satisfaction, a stronger reputation, and a more profitable bottom line. AI can also help banks better understand the root causes of complaints and develop more effective strategies to address and prevent them in the future.

By unlocking the rich context hiding in unstructured data such as call transcripts, chat logs, and customer surveys, AI tools can help banks enhance complaint responses through:

  • Chatbots that answer customer questions about complaints and provide initial support. This can help to reduce the number of complaints that require manual handling.
  • Machine learning to identify emerging patterns in complaint data and solve widespread issues faster.
  • Natural language processing to extract key information quickly.

However, banks may encounter roadblocks when integrating AI into their complaint-handling process. Data quality is essential for the success of any AI project but banks are often limited in their ability to find or label sufficient data. Banks cannot send their sensitive customer data to crowd labelers or to third-party models without compromising security.

Build better AI-fueled complaint handling with Snorkel AI

The Snorkel Flow data-centric AI platform helps businesses create and launch AI applications for their specific needs by enabling them to label data accurately and at scale. Snorkel Flow reduces costs and rapidly accelerates AI development in the following ways:

  • Labeling functions and weak supervision let users quickly build large, high-quality training sets programmatically while keeping humans in the loop to ensure that labeling schemas and logic align with business needs.
  • Cross-team collaboration in the platform allows experts from across the organization to encode their domain expertise into labeling functions. Snorkel’s weak supervision sorts through labeling function signals and determines which label is most likely correct.
  • Model explainability. Snorkel Flow gives users a trail of artifacts to help them understand how and why models return certain outputs. This helps users understand how to address errors and improve model accuracy.

Snorkel partners with leading cloud computing providers like Google Cloud and Microsoft Azure to help banks quickly and securely deploy their specialized AI applications. Banks can also choose to deploy on-premises, and always feel confident their data is secure due to Snorkel’s enterprise-grade security; Snorkel is proudly SOC2 Type 2 certified. 


How foundation models aid complaint resolution

The recent emergence of foundation models (FMs) has amplified AI’s ability to accomplish many tasks, including complaint handling. Publicly available large language model (LLM) APIs are a non-starter for banks due to regulations around sending sensitive data to third parties as well as the generalist API’s lack of domain-specific understanding.

For businesses to get the greatest value out of an LLM, they need to customize it with their proprietary data—something Snorkel can help with.

Thanks to the robust native integrations in Snorkel Flow, banks can also use best-in-breed foundation models (closed-API & open source) to boost their AI efforts by:

  • Labeling large training datasets faster. Banks using the Snorkel Flow platform can quickly generate high-quality labels for large datasets from relatively small hand-labeled sets to train their AI. Snorkel Flow can even suggest labeling functions automatically, so banks can significantly reduce the expert hours required to create training sets.
  • Training and distilling models. Banks can quickly refine models through iteration and human-in-the-loop collaboration, saving development time and resources. Then, they can distill that model’s expertise into a deployable form by having it “teach” a smaller model like BERT and applying it to their specific problem.

Banks can use these models to fine-tune their interactive voice responses and train conversational AI to automatically respond to queries over chat, email, and text. When automated responses fall short, banks can pair proprietary instruction-tuned large language models with the ability to search internal documentation to quickly deliver the information customer service agents need to resolve issues.

See how Snorkel can supercharge complaint handling

With Snorkel AI, banks can overcome the barriers to AI adoption and leverage the power of data-centric modeling to build more efficient, effective, and customer-centric complaint-handling processes. Snorkel enables business users with and without data expertise can to collaborate and combine their essential domain knowledge with the power of programmatic labeling to improve the accuracy and speed of their AI-enabled complaint resolution.

Ready to see how you can harness AI to enhance complaint handling, accelerate response time, automate communication, and reduce resource strain? Contact us today to learn more and get started.

Learn more

If you'd like to learn how the Snorkel AI team can help you develop high-quality LLMs or deliver value to your organization from generative AI, contact us to get started. See what Snorkel can do to accelerate your data science and machine learning teams. Book a demo today.