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AI Behavioural Analysis in Banking: How Behavioral Signals is Changing Customer Interaction

Rana Gujral is an entrepreneur, speaker, investor, and CEO at Behavioral Signals, an enterprise software company that excels at distinguishing behavioural signals in speech data with its proprietary deep learning technology. The company integrates Emotion AI in their communications with customers, using emotion and behaviour recognition to match each customer with the right agent to achieve better outcomes. 

Here The Fintech Times speaks to Rana about this technology and how AI is changing how banks communicate with customers.

Rana Gujral, EO at Behavioral Signals,

Tell us more about Behavioral Signals

Founded in 2016, Behavioral Signals enhances communication between humans and humans-to-machine by deducing intelligent and actionable insights from voice using deep learning and NLP. We offer a new level of communication with customers by leveraging our advanced acoustics engine to not only discover genuine emotion but also predict the speaker’s intent via behavioural analysis.

Our technology helps analyse human emotions and behaviours, transform data into usable information, improve human conversations, and drives increased profits. Until now, human emotion has been considered impossible to quantify and impossible to measure. With our patented analytics engine, we measure and interpret the “how” part of human interactions, regardless of language spoken. Our flagship product, AI-Mediated Conversations (AI-MC), is built specifically for the financial sector and involves building profiles of customers and call-centre agents to match them for future interactions.

What is Emotion AI?

Emotional artificial intelligence, also called Emotion AI or affective computing, is being used to develop machines that are capable of reading, interpreting, responding to, and imitating human affect. Companies are looking for ways to incorporate emotional intelligence into chatbots, virtual assistants, and other digital communication systems. As a result of meeting these demands, many companies are turning to automated chatbots driven by AI to help consumers get the information they need or call routing systems empowered by AI to build better communications. We need emotional intelligence to communicate with customers, patients, and product users in a more humane way.

How does the recognition software match customers with agents?

Behavioral Signals’ AI-MC solution involves building profiles of customers and call-centre agents based on past interactions. These profiles are fed into a predictive model to determine which agent should be paired with a specific customer in the future so that the desired outcome is achieved. Behavioural profiles comprise a set of behaviour and emotion related metrics reflecting, for example, whether a customer is negative, polite, or if they have shown any tendency to get easily agitated. Measurements of this kind are extracted from patterns identified in one’s voice and are based on emotion AI, namely the capability of the machine to understand the emotional state and intentions of humans.

The predictive model that is then employed essentially assesses the compatibility of all possible profile pairs and makes specific recommendations regarding who should speak to whom in a given context. It is an example of limited memory AI and has been trained using machine learning and a few thousands of past interactions associated with their corresponding outcomes. Fine-tuning the model is also possible and can lead to maximising a specific Key Performance Indicator (KPI) of interest, as, for example, customer’s propensity to buy in an inside sales call.

How is this helpful to financial institutions?

The number of non-performing loans (NPLs) during the 2009 Financial Crisis doubled from pre-crisis levels. The COVID-19 pandemic has thrown these levels into staggering heights. By the end of 2020, the US had $127.6billion (up from $95billion end of 2019) in NPLs, and Europe was battling a €401bn wave of new in-the-red loans. Entering 2021, this will represent a significant strain on the banks. At the same time, the current methods of debt collection in contact centres are inefficient and largely driven by human operators and analysis. AI-Mediated Conversations and predictive models that evaluate customers in real-time can help to transform this process with better profiling and prioritisation for the restructuring of these NPLs.

Currently, call centre efforts are largely reactive. Someone calls in, they are upset, and humans respond accordingly, not always in the most effective way. Algorithms can be used to rapidly evaluate and identify the behavioural patterns of these customers – based on previous calls and observed emotional data during a call. This enables faster call pairing to the best-suited customer service agent to address the specific problem.

One can also create predictive models for individual customers based on already established demographics like their age, the type of work they are in (and the likely impact of the current crisis on that line of work), their job title and salary, and a detailed history of their recent interactions with the bank. This allows for a predictive approach to debt collection – identifying high-risk debtors for restructuring and deprioritizing those who are considered lower risk.

What kind of scenarios is this technology the most useful in?

In a professional setting, we’re constantly meeting and interacting with new people. While our personal relationships are naturally curated – either by decades of familiarity or through personal preference in who we engage with – professional relationships are often hoisted upon us. We don’t choose who we work with or, to some extent, who we work for. To be successful, we must make these relationships work. There is always an optimal setting that allows both sides to achieve the desired outcome in a business environment. And that comes down to the natural rapport that develops between two people.

That rapport can be influenced by the right conversations and the right responses at the right times. Whether it’s a sales call with a prospect, a support conversation with a customer, or a difficult conversation in collections, business turns on the interactions of real humans. The affinity between any two humans is rarely ideal – most of us will get along better with some people than others, and it can influence the efficacy of business interaction.

So yes, better communication can influence a sale, resolve a complaint or even help with employee attrition.

AI seems to have a variety of different use cases in banking, do you think it will be commonplace in banking as we move into the future?

Implementing AI isn’t just a forward-thinking way to approach finances; it is a smart way to save money. The banking industry can streamline their operations and salvage over $440 billion by the year 2023 with the help of artificial intelligence technology.

But automation does not need to be heartless. On the contrary, AI can sound the alarm and stave off a financial crisis before it consumes us. The 2008 economic meltdown took the world by surprise, but AI could foresee another recession well before it strikes. By assessing non-performing loans (NPLs) and warning bankers before they reach a “point of no return,” AI can mitigate impending doom and secure smart, secure lending practices.

AI-MC has the potential to influence almost every aspect of our relationships and communications, from text messaging to sales and support conversations. By providing a means by which to enhance without assigning additional responsibility to the individuals in a conversation, we’re able to better match the right responses to the right moments.

The continued role of AI in predicting our intent, providing supplements to our conversations and relationships, and buffering against negative interactions will serve to enhance communication in significant ways in the future.

Do you think institutions are reluctant to take on this kind of AI, or are they ready to embrace changes?

The major concerns banks have with implementing a heavily AI-driven system in their call centres are data privacy, security, and customer satisfaction. Protecting our customers’ data and users’ personal information is critical to our business. Behavioral Signals has taken all measures to secure our clients’ data both from external and internal breach or misuse, as shown in our clean sheet SOC 2 compliance certification report.

What are your ambitions for the future of this technology?

Our ambitions include further developing our technology, investment in growth, and obviously incorporating our AI-MC solution in as many financial institutions, as possible, across the world.

We see education as the great equaliser, and we’re investing heavily in educating our potential clients as they embark on their AI journey. Staying ahead of the competition requires not only innovation but also the capacity to understand said innovation. The goal is to implement AI in their processes in such a way that both consumers and financial institutions can both benefit from its breakthrough potential.

AI-MC is here to stay. You can bank on it.

Author

  • Polly is a journalist, content creator and general opinion holder from North Wales. She has written for a number of publications, usually hovering around the topics of fintech, tech, lifestyle and body positivity.

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