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.
Use Of Chatbots
Chatbots and interactive voice response systems that utilize Natural Language Processing are increasingly used by banks nowadays to increase the efficiency of services. It reduces the expenditure on human capital, thus resulting in savings for the bank. Customer satisfaction is also enhanced as they can avail the service at the comfort of their homes without having to visit branches — saving their time. Chatbots can be enhanced in the future to announce new offers to the customer like loans, or to alert customers if they have any EMI payment due, suggest good discounts based on the tie-ups the bank has with e-commerce sites, etc.
Data analytics to predict future outcomes and trends:
Effortless and swift processing of a large amount of data can help banks observe the patterns of customer behavior, predict future outcomes and help them contact the right customer at the right time with the right product. It can also help in identifying frauds, fraudulent transactions, and simultaneously detect anti-money laundering pattern on a real-time basis.
Machine Learning and Cognition can be used to identify suspicious data patterns and convince banks if the actual source of money is legal or illegal. AI can also study the past consumer behavior to predict future requirements, which helps banks to up-sell and cross-sell successfully.
Wealth Management And Portfolio Management
AI-based systems help potential investors by analyzing their salary and spending patterns. They can also predict market trends and choose the right funds for their portfolio by determining the adequate sum of money they should invest every month in realizing their dreams. All this can be done without visiting branches or hiring experts. In the world of ‘Banking at your fingertips’, mutual funds, fixed deposits can be created at home, and the money is redeemed when necessary.
AI can further be leveraged to notify customers instantly for any suspicious transaction beyond their usual patterns.
Improving decision-making for loans and creditSimilarly, banks are using AI-based systems to help make more informed, safer, and profitable loan and credit decisions.
In addition to using data that are available, AI-based loan decision systems and machine learning algorithms can look at behaviors and patterns to determine if a customer with limited credit history might in fact make a good credit customer or find customers whose patterns might increase the likelihood of default.The challenge with using AI-based systems for loan and credit decisions is they can suffer from bias-related issues similar to their human counterparts.
Financial institutions operate under regulations that require them to issue explanations for their credit-issuing decisions to potential customers.
This makes it difficult to implement tools built around neural networks, which operate by teasing out subtle correlations between thousands of variables that are typically incomprehensible to the human mind.
AI in banking is being applied to these processes to eliminate much of the time-intensive and error-prone work involved in entering customer data from contracts, forms, and other sources.
Improved handwriting recognition, natural language processing, and other technologies, combined with intelligent process automation tools, are being used more and more in back-office operations to handle a wide range of banking workflows.In addition, by replacing these human processes with AI-based automation, banks can impose audit and regulatory control where it previously has been unable to do so.
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.
OpenAI is looking forward to this feat for both robotic add-ons and its own AI software, which allows Docktail to learn new things using virtual simulations, before overcoming a real, physical challenge.In a performance video featuring Docktail’s new talent, the robotic arm with clumsy yet precise maneuvers leads to a complete cube solution.
“Velander has been referring to a range of robots for the past few years that have pushed the Rubik’s Cube solution beyond the confines of human hands and minds.
In 2016, semiconductor manufacturer Infineon specifically developed a robot to solve the Rubik’s Cube at superhuman speed, and the bot was able to do so in under a second.
As Wallander notes, OpenAI’s ongoing robotics work is not only about achieving superior results in narrow tasks but also because you need to develop and program a good robot.
It has been trained using software that is currently trying to be fundamental to reflect the millions of years of evolution that help children learn to use our hands naturally.
One day, OpenI’s hopes will help mankind develop the humanoid robots we know only from science fiction, robots that can safely operate in society without risk, and perform a variety of tasks in a chaotic environment such as city streets and factory floors.To learn how to solve a Rubik’s Cube with one hand, OpenIII is not explicitly programmed by Docktail to fix the toy; Free software on the Internet can do it for you.
With this Big Data Parts of Digital Future technology becoming more ubiquitous, shoppers can make informed decisions using online data and content to discover, compare and buy products from anywhere, anytime.Uses of AI on Retail Industry in 2021: Image source: hacker noonChatbots to help with customer service:AI chatbots provide an even higher level of customer service, improve searches, send notifications about new collections, and suggest similar products.
Tommy Hilfiger and Burberry have launched chatbots to help their customers navigate their collections.Price adjustments:AI applications for retail stores could help companies set prices for their products, visualizing the likely results of multiple pricing strategies.
Mood tracking will definitely help build stronger customer relationships.Big Data Trends Revolutionizing Retail and Improving Retail Analytics: Image source: ZDNetRetailers can embrace a new geo-targeting phenomenon to better target potential customers within the store.
By utilizing the in-store sales funnel, marketers can come up with the best opportunities that deliver the highest ROI from their marketing efforts.Big Data allows retailers to feed their ideas, including cut, color and price, to an algorithm that uses sentiment analysis to predict whether or not the ideas will be a hit with consumers.It also helps retailers react more quickly to competition and electronic competition, market changes, and other macro-environmental factors by recognizing early signs of growing demand or slowing down in terms of sales.
It helps to assign sku to the stores where the demand would be and price it in a way, so it would bring the maximum gross margin.AI is transforming the retail industry in 2021: Image source: itechtionsThe AI Transforming your business in the Retail Industry has seen a massive change in its base of operations in recent times, with many large brands choosing to reduce the number of on-site offerings in favor of online service.
The reason for this is simple, customers are now looking for the path of least resistance when buying their clothes, and this is best reported on the Internet.
