
Voice technology helps compliance teams by converting call data into text. Calls in text format are then fed into compliance workflows where compliance breaches within calls can be automatically monitored and flagged.
Audio and speech captured in digital form increases every year. Combined with growing regulatory scrutiny and better capabilities when it comes to holding and discovering against audio channels, this creates new obligations and opportunities for many organizations.
No other channel provides such a rich and nuanced view of interactions between parties as voice. Concepts such as sentiment, intent, and inflexion can give clues to the meaning of a discussion and the state of mind of the participants – and are crucial for managing risk and deriving insight.
With each interaction, key information representing a business transaction, sale or trade are often discussed. This creates potential risk for compliance, privacy and security – and as organizations are forced into ever-decentralized operations as a result of the COVID-19 pandemic, the challenges only increase.
These challenges require solutions that can accurately process and understand speech-based content at scale. It means that solutions need to analyze large volumes of audio – and have the ability to support real-time interactions. This can only be done by leveraging the latest advances in machine learning, and industry-leading speech analytics built on deep neural network models.
Looking at the scale of compliance management from a banking context, estimates suggest that top-tier US banks are spending more than $1 billion a year on compliance, which in some cases accounts for more than 10% of a bank’s operating costs. For European banks, the average cost of compliance is estimated at 4% of total revenue – but is expected to rise to 10% by 2022. An Accenture survey validates this claim and anticipates an 89% increase in compliance investment over the next two years. It is expected that the operational regulatory burden facing financial institutions will double every few years.
Historically, when calls were recorded, compliance teams would manually review the calls for anomalies. However, this was not scalable. It’s labor-intensive, expensive and unnecessary. Every hour of audio took approximately three hours to review. Multiply this workload by the number of calls that take place daily and soon the simple task of listening to calls and identifying non-compliance issues becomes monumental.
Small-scale sampling of calls yields about a 3-5% review rate. This leaves financial services businesses and other organizations that deal with sensitive data incredibly exposed – with a data blind spot of over 95%.
Regulatory technology (RegTech) using accurate speech-to-text transcription can revolutionize this practice. In conjunction with analytics, organizations can transform 100% of their calls into a text-based format, allowing machine processes to automatically review, monitor and flag compliance breaches within calls.
By transcribing all calls into text, the heavy lifting is automated – leaving just the review process to be actioned by humans. This streamlined approach to compliance monitoring using automatic voice technology means organizations can become more compliant while also reducing manual time spent on case reviews.
An investment in speech-to-text technology helps compliance monitoring teams by giving them better visibility of interactions and providing them with the input to power additional tools – like NLP solutions – so they can be more productive and efficient. With speech-to-text, teams can review more calls, faster and in a cost-effective way to spot potential compliance issues and mitigate fines.
By transforming interactions into text, compliance teams can create a more concise audit trail, making eDiscovery faster and easier. Being able to retrieve information rapidly accelerates eDiscovery timelines when a breach does happen, helping to minimize fines.
Compliance automation is essential for organizations – streamlining compliance workflows and enabling them to operate a compliant business daily, not just occasionally. Instead of looking for a needle in a haystack – listening to hundreds of calls to find something of interest – compliance teams can use keyword searches to go straight to the needle.
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On Arabic and English, Melia 1 runs at less than half the mixed error rate of the next best model. On Mandarin and Tamil it switches more accurately than anything else we tested.
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Linden, Speechmatics' new speech-to-text model built for voice agents, is now available through LiveKit Inference, no separate API key, account, or invoice required. Try it out now, click the circle to the bottom right of your screen.

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