Revolutionising Fintech with AI: Integrating Chatbots with Intelligence in Business

 

With half the year gone, the year 2020 is expected to witness a sharp rise in the adoption of Artificial Intelligence (AI) in sectors other than just scientific sectors. Areas where huge monetary numbers are at play every day needs to be more protected and made robust enough to not fall prey to any financial treachery. Adoption of AI in fintech is therefore, aimed at improving the accuracy, personalisation of payment, lending services, and insurance services along with unveiling new borrower pools. It is interesting to point out that some fintech companies have incorporated AI into their structures already for some time now as financial firms were the early adopters of mainframe computer, relational databases, and has always been at the forefront to espouse next level computational power.

Fintech is among the fastest-growing industries, owing to the increasing penetration of internet users. The emergence of digital payment solutions has resulted in a paradigm shift in the usage of mobile devices for financial transactions and related actions.

Behind this gigantic boom in the fintech market, several futuristic technologies like AI and ML are the major contributor in making the whole system faster, robust, securer, and scalable.  According to Mordor Intelligence, artificial intelligence in fintech market is estimated to touch over USD 35.40 billion by 2025.

Here are some pivotal applications of AI in Fintech that have disrupted the traditional methods and operations with some amazing case studies:

Fraud Detection and Compliance

According to the Alan Turing Institute, with $70 billion USD spent by financial institutes on fraud detection and compliance each year and this is just in the US; the amount of money spent on the world level for fraud is staggering. Between 2015 and 2016, the UK alone witnessed exponential increase of payments-related fraud by 66%, indicating the problem clearly and is obviously much more than a momentary phenomenon.

With the advent of third-party applications like Whatsapp that have incorporated in-app transaction system has driven the expected frauds and illicit transactions. AI is a pioneering technology that is being heavily used in detecting frauds. The technology is capable of analysing millions of data sets in few seconds to spot anomalous transactional patterns. Once these apprehensive activities are gathered, AI-backed models determine whether they were just unintentional mistakes or fraudulent activities.

Alibaba, a Chinese giant that equips AI-based fraud detection model in the form of a customer chatbot called Alipay. While, Mastercard employs its brand-new Decision Intelligence (DI) technology to scrutinize historical payments data gathered from each customer identifying and prevent credit card fraud in real-time.

Fighting Against Money Laundering

Tracing unknown money laundering and terrorist financing schemes have always been the biggest challenge faced by finance institutes. Most of the sophisticated financial crime patterns often break the rigid conventional rules-based systems used by many fintech companies. Moreover, the deficient public datasets that are usually large in size makes fighting against money laundering even more intricate, ultimately resulting into myriads of false positive results.

ML algorithms and artificial neural networks (ANN) have shown great results by outperforming any conventional statistic system in spotting suspicious activities. ThetaRay, a cyber security and big data analytics company leveraged advanced unsupervised ML algorithms in tandem with big data and analytics to observe and analyse numerous data sources like present customer behaviour vs. existing behaviour. Doing so, helped the company to successfully perceive the most complex money laundering and terrorist financing patterns. It included abnormal cash deposits in high risk countries, transfers from tax-havens countries, and multiple accounts controlled by common beneficiaries to hide black cash transfers.

Automated Customer Support

Today, customers share their problems with systems like text chats, voice systems, and Chatbots receiving human-like customer service and expert’s advice experience at a low cost and less time. Other than health, the finance sector can never lose a grip over customer support and therefore, both the sectors consider chatbots backed by AI.

Companies like Kasisto works with a conversational AI chatbot that can solve customer queries regarding their past expenses, current balance, and personal savings. In 2017, Alibaba’s Ant Financial’s chatbot system surpassed human performance in customer support.

Moreover, Alibaba’s AI-based customer service Alipay tackles 2 million to 3 million user queries per day. As of 2018, the system alone successfully completed five rounds of inquiries in mere one second. Other companies like Tryg uses conversational AI named Rosa which is an incredibly efficient virtual agent that alternates inexperienced employees with her expert guidance.

According to a recent report of McKinsey, banks alone were expected to invest over $5.6 billion USD on AI and Machine Learning (ML) solutions in 2019; just a fraction of what they were expected to get in return as profits generated which was $250 billion USD in value. While as per the latest 84-page report, by 2030, traditional financial institutions can curtail over 22% in overall cost by incorporating AI in the financial industry. AI and ML have become incredibly helpful to alleviate numerous cumbersome operations, detect fraudulent acts, accentuate customer experience, and even help employees understand erratic customer behaviour and trends.