13th June 2020
Recent years have seen a rapid acceleration in the pace of disruptive technologies, thanks to AI (artificial intelligence). The fintech industry, specifically, has seen a steep rise in the use cases of ML (machine learning) applications to advance better outcomes for both consumers and businesses.
Trends are poised to become industry standards and there is a reinvigorated focus on consumer-friendly tech. Naturally, AI and ML are at the heart of this, and it comes as no surprise that the industry is predicted to be worth over $17,440 million with a CAGR of 17.9% by 2027. The only question is, how will companies use these tools to implement digital transformation and more importantly make it count?
Right from speeding up the underwriting process, portfolio composition, optimization, model validation, and market impact analysis, to offering alternative credit reporting methods, the different use cases of Artificial Intelligence and Machine Learning are having a significant impact on the financial sector.
The finance industry, including the banks, trading, and fintech firms, are rapidly deploying machine algorithms to automate time-consuming, mundane processes, and offering a far more streamlined and personalized customer experience.
Alongside knowing what your customers want is communicating with them. Nowadays, consumers expect response times to be faster and more convenient for them, no more office hours — 24/7 communication is the new normal for many. However, for many businesses, it’s almost impossible to ensure round-the-clock communications, and this is where conversation AI is coming in.
With an estimated 3,150% growth rate in terms of successful chatbot interactions between 2019 and 2023 and an estimated 862 million hours saved for businesses in the future, it’s clear that chatbots will continue to impact how business communication is done in the future. Conversational AI is transforming chatbots from a stopgap in consumer communications to a genuinely useful tool to help consumers, and this is something we are likely to see more of in coming years as AI techniques are able to make chats more “human.”
At Rootcode AI, our team of Data Scientists, Machine Learning Engineers, and Data Engineers built “ConverseAI” - an intelligent conversational AI assistant that can help e-commerce users with their inquiries, suggest them products, and help resolve their questions about products, reducing the response time to customers from hours to seconds providing an overall superior customer support experience. ConverseAI’s conversational engine was integrated into one of the leading cosmetics brands, which now handles 95% of the customer queries.
The core offering of any AI solution rests in its ability to methodically analyze data and generate relevant insights that are otherwise invisible to the human eye. Any form of analysis becomes all the more convoluted when it involves multiple variables and dynamic parameters.
However, AI remains unperturbed by changes and manages to recognize patterns, analyze cash flows, flag suspicious activities, and detect fraud. As a result, businesses can use it for various applications ranging from budgeting to transaction validation to credit scoring and more.
Another way how AI is helpful in analyzing different asset classes is by providing sentiment analysis. AI tools are renowned for their ability to provide accurate sentiment analysis. This is helpful for investors who are momentum traders and are interested in investing in a stock that has an improving sentiment.
Forecasting has proven to be a highly sought-after quality throughout the F&PA (Financial and Public Administrations) market, and AI makes it possible with its data-driven approach. Hence, it should come as no surprise that predictive analysis is one of the most well-known and well-loved limbs of AI in F&PA.
It finds widespread applications across different verticals, ranging from predicting customer behavior to forecasting project spends. These can even power financial advisory tools that may calibrate spending habits based on lifestyle indicators. Similarly, it can act as the north star for intraday traders to time the market and take actions like wait, increase position or withdraw depending on their risk appetite, opening and closing prices, the possibility of profit/loss, etc.
For instance, an AI is used to make revenue projections. This is especially helpful for investors who are investing based on earnings announcements. AI that can largely accurately predict future revenue can be invaluable for investors.
While analysis is oriented to the present conditions, forecasting helps with future predictions. And planning acts as a bridge that aligns the two states.
AI can help companies and individuals draw a practical road map that connects their current state to their future aspirations. Say, it can power Robo-advisories that guide customers on investments to reach their retirement or financial independence goals. Similarly, AI can help businesses factor in any exigencies that may impact the budget of any project and how to tackle the same. The actionable inputs offered by AI ensure that the planning process follows the shortest course while realizing objectives.
For example, an exposure analysis tool can generate a list of assets that are susceptible to certain market exposures, which can help investors in planning their portfolios.
The Fintech industry is a hotspot for AI-led technological innovation. Companies can accordingly deploy AI solutions to explore the various drivers for their business, create accurate forecasts, enhance real-time decision-making and improve ROI.
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