5 important ways conversation AI startups are driving change across the financial services industry
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Emerging voice AI technology has already made significant strides solving for privacy, security, efficiency, equality, and personalization in the world of finance. Conversation intelligence adoption is accelerating at lightning speed in this sector, so it’s likely that we’ll all engage with some form of it when checking in on, forecasting or shifting around finances in the near future.
Per Forbes, the third quarter of 2021 was actually the second-highest on record for FinTech financing — a whopping 147% increase year-over-year! The article goes on to list blockchain and Web3 as two primary drivers of this increase, but notes that today’s FinTech trends are all fundamentally connected by their ability to make the lives of customers and business processes run smoothly.
Processes that were at one point in time rife with friction such as opening a bank account, transferring money, retirement planning, investing, and applying for loans are all being made easier thanks to advances in artificial intelligence across finance verticals. Below are five important ways innovative players in conversation intelligence are furthering the hype around AI in finance.
Voice recognition and natural language processing technologies have already proven to be successful when applied to financial institutions’ customer support services. They can reduce operating costs, improve the customer experience, and create the opportunity for ultra-valuable voice data mining.
For a financial institution that has already adopted conversation AI technology, any given customer interaction is a veritable goldmine of data. Once a company reaches critical mass in terms of their conversation AI-backed database, important trends, problems and patterns arise naturally.
Millennials and Gen Z expect nothing less than top-notch omnichannel interactions with brands, which is likely why — according to Intellias—optimizing the customer experience is the new battleground for FinTech startups across the board. Younger consumers desire simpler, more intuitive ways to achieve their financial goals, and conversation AI can support personalization in this way as well as keep customers and customer-facing employees on track with coaching capabilities.
Compliance and security
Voice AI technologies can improve upon a financial institution’s customer and employee oversight by flagging behavior on either end that is suspicious, inappropriate, or otherwise not compliant with company rules. Research suggests that finance workers are ten times more likely to share insider information or make unprofessional comments on phone or video calls as opposed to text-based conversations.
DataBricks reports that AI and machine learning can streamline compliance alert systems to near perfection, and that is a huge deal. Large banks, for instance, experience false positives in their compliance systems at rates that sometimes exceed 90% and these cases are then placed in a long queue for a compliance professional to examine one by one. This is extremely inefficient.
Conversation intelligence can be used to ID and authenticate a caller’s identity, cracking down on potential instances of fraud, which has enormous implications for finance businesses because the fines for failing to meet ever-changing compliance regulations are expensive — the average fine in 2020 was $2 million! Additional compliance interactions supported by voice AI include mandatory compliance dialogues and Mini-Miranda rights.
Although customer interactions and compliance were some of the first obvious reasons for conversation intelligence adoption among traditional finance institutions, asset management and investment advice from machine learning algorithms that can factor in real-time data to maximize long-term gains have become increasingly commonplace as well.
A new aspect that voice AI startups bring to the table is the analysis of qualitative language data based on annual reports, quarterly earnings call transcripts or other conference transcripts, by which they can assess trends and inflection points when market conditions are turning.
One Symbl for Startups member using voice AI to offer users deeper insights into earnings conference calls is Helios. The company’s Comprehend: Elite product zeroes in on tone of voice, a huge indicator of sentiment and behavior that is missing from traditional text-based analysis. It is also the first and only voice-based tonal analysis data product for the financial industry.
Tone of voice may or may not align with the words that are spoken — many people have experience with this fact on a personal level — so it is helpful to gauge whether an executive’s tone of voice is stressed or confident independent of the script they provide. Vocal tone accounts for nearly 40% of human communication!
It’s clear at this stage that voice data is invaluable for risk mitigation endeavors; intelligent voice AI-supported algorithms can prevent unwanted risks associated with financial missteps and cybercrimes including money laundering, fraud, and unauthorized transactions. Today’s machine learning models can be built to protect against surprisingly complex risk factors.
However, this isn’t to say that classical risk management will be rendered obsolete, but rather it opens the door to a novel approach that combines AI technology and computing platforms with more traditional methods.
Forecasting and trend analysis
The ultimate goal of any investor is to be able to determine the sentiment of the market in real time and invest accordingly. FinTech startups are at the forefront of the AI-driven movement that can turn that goal into a reality for many. This is another key way Symbl for Startups’ Helios is upleveling earnings call analyses: Voice tone can inform analysts’ assessment of future investment returns, M&A moves, and overall outlook for future performance.
All of this being said, conversation intelligence technologies are meaningless in finance without an up-to-date regulatory framework that enforces close supervision of the sector’s emerging machine learning algorithms and the databases they rely on, with an emphasis on bias recognition. Without it, there could be dangerous lapses in transparency, safety and ethical standards as voice AI scales across established financial services businesses.