From electronic trading platforms to medical diagnosis, robot control, entertainment, education, health, and commerce, Artificial Intelligence (AI) and digital disruption have touched every field in the 21st century. AI has made its presence felt in all walks of life due to its ability to help the user innovate. It has also enabled users to make faster and more informed decisions with an increased amount of efficiency.
Of late, the banking sector is becoming an active adapter of artificial intelligence — exploring and implementing this technology in new ways. The penetration of artificial intelligence in the banking sector had been unnoticed and sluggish until the advent of the era of internet banking.
One of the first steps was taken in 2015 by Ally Bank (USA) — introducing Ally Assist — a chatbot that could respond to voice and text, make payments on behalf of the customer, give an account summary, monitor savings, spending patterns, and use natural language processing to understand and address customer queries.
Banks all over the world followed up with their best versions of chatbots: Erica to iPAL, Eva and the most famous one — SBI’s SIA. According to Payjo (the start-up which developed SIA), SIA can handle up to ten thousand inquiries per second and is one of the world’s largest deployments of artificial intelligence in consumer-facing banking. In this era of technological revolution, the banking sector has also witnessed a paradigm shift in its approach from brick and mortar branches to digital banks. Banks are increasingly spending on artificial intelligence and ML in data analytics for personalized and faster customer experiences to garner the interests of the tech-savvy and the millennial class.
To Know About: Rise of AI-Powered Chatbots in the Banking Industry
According to the FinTech Trends India Report by PwC in 2017, the global spending in artificial intelligence has touched $5.1 billion. The IHS Markit’s “Artificial intelligence in Banking” report claims that this cost has grown up to $41.1 billion in 2018, and is expected to reach $300 billion by 2030. This shows that artificial intelligence has reached a stage where it has become affordable and efficient enough for implementation in financial services. The challenge now is in exploring more ways where the powers of artificial intelligence can be harnessed to streamline internal banking processes and improve customer experiences.
Front end operations of artificial intelligence are those that involve direct interaction with the clients. It includes applications and payment interfaces, digital wallets, chatbots, or interactive voice response systems. Back-end operations are more complicated as they involve the systematic processing of large chunks or terabytes of data to provide security to the system, analyze fraudulent transactions, and generate reports, improve compliance. We shall now discuss the future of artificial intelligence in each of these fields.
Enhanced Customer Experience
The customer being the key driver of a service industry, customer service is at the forefront of any business. AI can be used to derive a better understanding of customers spending patterns, which will help banks customize products by adding personalized features. This supplements meaningful customer engagement, building strong relationships, and growth of business for the bank.
To Know about: Applications of artificial intelligence in banking
For instance, SBI is working on a system ‘Automated Real-Time Customer Emotion Feedback’ (ARTCEF) using AI to study, in real-time, the facial expressions of customers. AI can also be used to offer personalized payment experience — like most suitable EMIs at checkout based on past payment patterns, offering multi-currency cards to customers who frequently travel abroad, etc. AI can also help in setting up biometric face recognition ATMs which function without the need of a card, using real-time camera images and at the same time detect and prevent frauds.
To be contd in part-2..
From electronic trading platforms to medical diagnosis, robot control, entertainment, education, health, and commerce, Artificial Intelligence (AI) and digital disruption have touched every field in the 21st century.
It has also enabled users to make faster and more informed decisions with an increased amount of efficiency.Of late, the banking sector is becoming an active adapter of artificial intelligence — exploring and implementing this technology in new ways.
The penetration of artificial intelligence in the banking sector had been unnoticed and sluggish until the advent of the era of internet banking.One of the first steps was taken in 2015 by Ally Bank (USA) — introducing Ally Assist — a chatbot that could respond to voice and text, make payments on behalf of the customer, give an account summary, monitor savings, spending patterns, and use natural language processing to understand and address customer queries.Banks all over the world followed up with their best versions of chatbots: Erica to iPAL, Eva and the most famous one — SBI’s SIA.
According to Payjo (the start-up which developed SIA), SIA can handle up to ten thousand inquiries per second and is one of the world’s largest deployments of artificial intelligence in consumer-facing banking.
Banks are increasingly spending on artificial intelligence and ML in data analytics for personalized and faster customer experiences to garner the interests of the tech-savvy and the millennial class.To Know About: Rise of AI-Powered Chatbots in the Banking IndustryAccording to the FinTech Trends India Report by PwC in 2017, the global spending in artificial intelligence has touched $5.1 billion.
It includes applications and payment interfaces, digital wallets, chatbots, or interactive voice response systems.
An effective end-to-end CCM solution can also help banks reduce communication costs.Increasing client communications can often mean sending more communications and making it harder to estimate the cost.
What if clients agree to have digital communication, but are not fully paperless?
These ‘double dippers’ want digital communication facilities but are not yet ready to give up the habit of receiving envelopes in the mail.
This doubling of costs, while positive for your omnichannel communication strategy, can be devastating to your budget.In two years, the percentage of digital users alone is expected to increase to 36%, down from 31%, at a print-only price.
This means that banks must either adjust their communications budgets to accommodate double dumpers or find innovative ways to encourage them to go completely paperless.Seek cost confirmation while preserving client engagement.Unexpected communication costs can wreak havoc with the bank’s annual budget.
ROI calculations become more accurate when the annual fee (ex-post) is fixed.All you need is cost assurance — an expected fee structure that covers all communications in print and digital.
In 2020, when the world was devasted by the deadliest pandemic of all the centuries, we saw a rise in the adoption rate of digital banking services.
As per the Mckinsey report, a 20 to 50 per cent rise in adoption was recorded.
AI being an integral part of daily life, it becomes necessary to be AI-First to remain relevant to the changing market conditions.
To stay relevant, to stay unique, to compete and compel the ever-changing, tech-savvy customers, banks need to deploy the latest technologies at scale.
Conversational bankingIn conversational banking, banks leverage AI to engage with customers with chatbots and Robo advisors to recommend products that will be totally personalised.
To deepen the customer relationships, AI is deployed at scale in the front office and structure.
Even in the 2010s quite a lot of American retail website users suffered from online transaction frauds until the two-step verification came into power while shopping online.However, credit card frauds are still a challenge for money transaction platform owners or small to global retail services and hundreds of online frauds keep popping up daily as long as unauthorized card transactions hit the record amount of 16.7 million victims in 2017 (as reported by Javelin Strategy & Research).
Additionally, according to the Federal Trade Commission (FTC), the number of credit card fraud claims in 2017 overcame the previous year’s number of frauds by 40%.
) Among these Fraud Detection and Prevention techniques, predictive Machine Learning models are the one that belongs to smart Internet security solutions.
After the patterns are discovered, there is a Pattern Recognition phase which can detect classes, clusters, and patterns of suspicious behavior.
For example, the Neural Networks approach helps to automatically identify characteristics found in fraud, which is the most effective if you have a lot of transaction samples.
Whenever a user requests a transaction, it is being processed for some time and depending on the level of predicted fraud probability there are 3 kinds of possible outputs:If the probability is less than 10%, the transaction is allowed;If the probability is between 10% and 80%, additional authentication factor (one time SMS code, Fingerprint, Secret Question) should be applied;If the probability is more than 80%, the transaction is frozen, so it should be processed manually.
AI, coupled with the rich data sets available in the financial services sector, provides organizations with the means of defending their businesses and defeating criminals.
Let’s have a look at how.
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In the list of scenarios and instances that feel nostalgic today, we could clearly add visiting the bank.
Today, visits to banks are only limited to circumstances that involve paperwork for loans, approvals & statements that require physical verification.
Otherwise, pretty much every banking-related action can be carried out through apps or online portals.
Digital transformation in banking has made our lives simpler instances of stepping out and personally visiting a bank.
Today, we have IoT-enabled banking services, tap and chip-based cards with added encryptions & app-based payment systems.
From the elderly to the young population, banking has become accessible, banking functions more understandable and services more streamlined.
