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Picture that your business conducts outbound marketing calls. By employing Spoken Language Processing (SLP), you can monitor if fair marketing and sales techniques are being used by your agents in real time. Not only, will this improve your customer service, but it will also help you avoid misconduct like upselling, pressure selling or pressure retention.
Spoken Language Processing (SLP) is a subdomain of Natural Language and Speech Processing which can comprehend colloquial language occurring in spontaneous conversation or informal written language. Technological advances in SLP have presented new opportunities to improve business processes, one of which is its application in contact centre operations.
Contact centres are still a significant operational cost for businesses. Actions like checking a balance or paying a bill can be fully automated using integrated chatbots or social media, given the simplistic and routine nature of the task. However, more complex enquiries such as complaints require a deeper understanding of the issue and human empathy. In such scenarios, operational efficiency can be largely improved by assistive technologies like SLP.
The above diagram conceptually summarises the potential application of SLP in businesses, specifically within contact centers. Other than operations, SLP also has a strategic role in helping businesses to comply with mandated regulations.
AI solutions based on language technology can offer real-time monitor agent performance. For example, they can check if the agent is following standard procedures and if the information being provided to the customer is accurate, adequate, and relevant. SLP systems can recognise customer intention and recommend the most effective response or procedure to an agent. This assistive technology can also be designed to constantly improve responses.
Capturing customer information over phone such as verification, underwriting, or assessment of risk is often challenging, due to low voice quality and human error. Using real-time transcriptions to capture customer information(such as name, account number, date of birth, bank account, contact details) reduces the risk of human error while improving time efficiency.
Real-time monitoring and analysis of customer emotions can significantly uplift contact centre performance.Customer voice can be analysed to detect stress, social pressures, confusion, hesitation, distraction and anger. These insights captured can be fed into the back end database or flagged in real-time to reduce workload and increase quality of services being delivered, whilst meeting regulatory compliance.
SLP has further strategic potential when applied to areas of law enforcement and regulations in contact centres. Banks, insurers, creditors, and other financial institutions in Australia must comply with regulations such as KYC (Know Your Customer) and AML (Anti-Money Laundry) . KYC, as the name implies is the requirement to conduct ongoing due diligence on customers using available sources of data. This includes using phone calls to verify customers’ identity using their biometric voiceprints or to identify risks associated with money laundering. This extends beyond transactional activities and requires monitoring of behavioural information over time. Imagine being able to identify a nervous customer who calls a bank to make a suspicious transaction or indicates the transaction is being conducted on someone else’s behalf. SLP enables enterprises to capture pieces of sensitive information for behavioural analysis, which leads to a holistic due diligence process in-place.
What is Deloitte doing in this space?
We are working with our clients to navigate the complex and fast evolving financial regulations in place through SLP. We have the market-leading capabilities to enable our clients to embrace and implement assistive SLP technologies for both strategic and operational purposes while also uplifting the customer experience.
Want to learn more? Get in touch with us today to kickstart your SLP journey with Deloitte.
Bahram is an AI/ML specialist and thought leader with a long record of projects delivered across industries with culture of leadership, excellence, and innovation. He has extensive experience across platforms and technologies to deliver value and provisioning strategic data driven insight. Before joining industry, Bahram was with university as an academic with tens of peer reviewed publications and patent in the area of ML and NLP- teaching ML/NLP courses and supervising graduate projects.