How artificial intelligence is changing the face of banking in India
Artificial intelligence (AI) will empower banking organisations to completely redefine how they operate, establish innovative products and services, and most importantly impact customer experience interventions. In this second machine age, banks will find themselves competing with upstart fintech firms leveraging advanced technologies that augment or even replace human workers with sophisticated algorithms. To maintain a sharp competitive edge, banking corporations will need to embrace AI and weave it into their business strategy.
In this post, I will examine the dynamics of AI ecosystems in the banking industry and how it is fast becoming a major disrupter by looking at some of the critical unsolved problems in this area of business. AI’s potential can be looked at through multiple lenses in this sector, particularly its implications and applications across the operating landscape of banking. Let us focus on some of the key artifiicial intelligence technology systems: robotics, computer vision, language, virtual agents, and machine learning (including deep learning) that underlines many recent advances made in this sector.
Banks entering the intelligence age are under intense pressure on multiple fronts. Rapid advances in AI are coming at a time of widespread technological and digital disruption. To manage this impact, many changes are being triggered.
- Leading banks are aggressively hiring Chief AI Officers while investing in AI labs and incubators
- AI-powered banking bots are being used on the customer experience front.
- Intelligent personal investment products are available at scale
- Multiple banks are moving towards custom in-house solutions that leverage sophisticated ontologies, natural language processing, machine learning, pattern recognition, and probabilistic reasoning algorithms to aid skilled employees and robots with complex decisions
Some of the key characteristics shaping this industry include:
- Decision support and advanced algorithms allow the automation of processes that are more cognitive in nature
- Solutions incorporate advanced self-learning capabilities
- Sophisticated cognitive hypothesis generation/advanced predictive analytics
Surge of AI in Banking
Banks today are struggling to reduce costs, meet margins, and exceed customer expectations through personal experience. To enable this, implementing AI is particularly important. And banks have started embracing AI and related technologies worldwide. According to a survey by the National Business Research Institute, over 32 percent of financial institutions use AI through voice recognition and predictive analysis. The dawn of mobile technology, data availability and the explosion of open-source software provides artificial intelligence huge playing field in the banking sector. The changing dynamics of an app-driven world is enabling the banking sector to leverage AI and integrate it tightly with the business imperatives.
AI in Banking Customer Services
Automated AI-powered customer service is gaining strong traction. Using data gathered from users’ devices, AI-based relay information using machine learning by redirecting users to the source. AI-related features also enable services, offers, and insights in line with the user’s behaviour and requirements. The cognitive machine is trained to advise and communicate by analysing users’ data. Online wealth management services and other services are powered by integrating AI advancements to the app by capturing relevant data.
The tested example of answering simple questions that the users have and redirecting them to the relevant resource has proven successful. Routine and basic operations i.e. opening or closing the account, transfer of funds, can be enabled with the help of chatbots.
Fraud and risk management
Online fraud is an area of massive concern for businesses as they digitise at scale. Risk management at internet scale cannot be managed manually or by using legacy information systems. Most banks are looking to deploy machine or deep learning and predictive analytics to examine all transactions in real-time. Machine learning can play an extremely critical role in the bank’s middle office.
The primary uses include mitigating fraud by scanning transactions for suspicious patterns in real-time, measuring clients for creditworthiness, and enabling risk analysts with right recommendations for curbing risk.
Trading and Securities
Robotic Process Automation (RPA) plays a key role in security settlement through reconciliation and validation of information in the back office with trades enabled in the front office. Artificial intelligence facilitates the overall process of trade enrichment, confirmation and settlement.
Lending is a critical business for banks, which directly and indirectly touches almost all parts of the economy. At its core, lending can be seen as a big data problem. This makes it an effective case for machine learning. One of the critical aspects is the validation of creditworthiness of individuals or businesses seeking such loans. The more data available about the borrower, the better you can assess their creditworthiness.
Usually, the amount of a loan is tied to assessments based on the value of the collateral and taking future inflation into consideration. The potential of AI is that it can analyse all of these data sources together to generate a coherent decision. In fact, banks today look at creditworthiness as one of their everyday applications of AI.
Banks are increasingly relying on machine learning to make smarter, real-time investment decisions on behalf of their investors and clients.
These algorithms can progress across distinct ways. Data becomes an integral part of their decision-making tree, this enables them to experiment with different strategies on the fly to broaden their focus to consider a more diverse range of assets.
Banks are focussed to leverage an AI and machine learning-based technology platforms that make customised portfolio profiles of customers based on their investment limits, patterns and preferences.
Banking and artificial intelligence are at a vantage position to unleash the next wave of digital disruption. A user-friendly AI ecosystem has the potential for creating value for the banking industry, but the desire to adopt such solutions across all spectrums can become roadblocks. Some of the issues can be long implementation timelines, limitations in the budgeting process, reliance on legacy platforms, and the overall complexity of a bank’s technology environment.
To overcome the above challenges of introducing and building an AI-enabled environment. Banks need to enable incremental adoption methods and technologies. The critical part is ensuring that the transition allows them to overcome the change management/behavioural issues. The secret sauce of successful deployment is to ensure a seamless fit into the existing technology architecture landscape, making an effective AI enterprise environment.
How Startups can leverage AI to gain competitive advantage
Despite nationwide venture funding hitting a multiyear low, venture capital deployed to artificial intelligence (AI) startups has reached a record high.
Last year, VCs struck 658 deals with AI companies, nearly five times the number that signed on the dotted line four years before. To date, the market contains 2,045 AI startups and more than 17,000 market followers, with more joining by the day.
AI’s rapid rise has swept up startups and enterprises alike, including U.S. automaker Ford, which recently bought AI startup Argo for $1 billion. The acquisition cements experts’ suspicions of Ford’s coming foray into self-driving technology. Other startups — so many, in fact, that entrepreneurs need a “best of” guide — are betting heavily on bot platforms.
So while we’ve just glimpsed the tip of this innovation iceberg, it’s clear AI is no longer some nebulous technology of the future. Sixty-eight percent of marketing executives, report using AI in their operations. For a technology that only went mainstream in 2016 and barely existed four years ago, that’s a remarkable adoption rate. How, regardless of the platform you choose, can you join forward-thinking entrepreneurs and build your business with AI? Over the last few years , I have worked closely with multiple start ups across genres and ,So far, four ways stand out:
1. Get to know your next customer.
A politician wouldn’t dream of delivering a small-town stump speech to her urban constituents. Why? Because you’ve got to know your audience. The same is true for entrepreneurs. Before you broadcast your message, you need to know who you’re trying to reach.
Node, an account-based intelligence startup, uses natural language processing — a fancy term for teaching a computer to understand how we humans speak and write — to develop customer profiles. Node is crunching vast swaths of data to connect the dots between marketers and the companies they’re trying to reach.
Once you have ample customer data — Node uses data crawlers to scrape information from social media, news sites and more — pair machine learning and natural language processing models to extract sentiments from unstructured data. Then, just as senators segment constituents into demographic groups, Node uses cluster analysis to sort clients’ customers into like cohorts.
2. See how people truly use your product.
If, heaven forbid, you forgot to tag your neighbor at last week’s house party, Facebook was no doubt there to remind you of your error. How does Facebook know which of your friends you left untagged? It has gone all-in on an AI technique called convolutional neural networks.
Convolutional neural networks, which loosely model how the brain’s visual cortex interacts with the eyes, work by separating an image into tiny portions before running each of those specks through a multilayered filter. It then “sees” where each speck overlaps with other parts of the image, and through automated iterations, it puts together a full image.
Many different ways exist to apply this technology, but retail businesses can start with image classification. Try using a convolutional neural network to break down photos of your products posted online. The model can identify customer segments that frequently use your product, where they’re using it and whether they commonly pair other products with yours. Essentially, this automated image analysis can show you how your products fit into customers’ lives, allowing you to tailor your marketing materials to fit.
3. Get inside the user’s head.
Success on social media requires careful listening and quick action. When a social campaign isn’t working, it’s best to put it out of its misery quickly. On the other hand, when one strikes a chord with customers, doubling down pays dividends.
But to do so, you need real-time insights about customers’ reactions to your content. Fortunately, AI can take the emotional temperature of thousands of customers at once. Dumbstruck, a video-testing and analytics startup that I advise, has added natural language processing to its emotional analytics stack. This allows it to provide moment-by-moment insights into viewers’ reactions to media. Dumbstruck’s model grows stronger with each reaction analyzed, producing a program that perceives human emotions even better than some people can.
4. Provide affordable, always-on support.
Customer service is — or should be, according to consumers — the department that never sleeps. More than half of people, 50.6 percent to be precise, believe a business should be available 24/7 to answer their every question and concern. When asked whether businesses should be available via a messaging app, the “yes” votes jump to nearly two in three.
Fortunately, bots don’t sleep, eat or go off-script. A well-built bot can offer cost-effective, constant customer service. Of course, grooming your bot to serve customers requires front-end data — ideally hundreds of thousands of example conversations — but you can get started with a human-chatbot hybrid. With this approach, the bot answers run-of-the-mill questions, while a human takes over for the more complex ones. Then, as the data builds and the model matures, you can phase in full automation.
AI’s Impact on small businesses and startups
Small enterprises will begin to use the tried and tested platforms in innovative ways. While startups will gain a competitive edge in capturing the AI market, the larger enterprises will provide the infrastructure to startups for building innovative services. It is somewhat similar to the business model followed when the cable technology was introduced.
Startups leveraging AI technology for industry verticals, like agriculture, manufacturing or insurance are bound to be successful.
Startups can empower established insurance companies like State Farm, Allstate and Farmers with technology enabling them to become more proactive in policy planning. For instance, a new AI insurance underwriter will help to forecast natural disasters and accidents, and adjust premiums.
The predictive decision-making capabilities are more than just a novel technology. You can manage food supply chains with the help of AI. Startups could develop end-to-end farming solutions with AI analytics for reducing food waste. It will have a huge impact in tackling global issues of hunger and famine.
Whether serving as a research assistant in a large corporation, acting as a voice-activated resource in difficult medical procedures, AI is fast becoming a reality. The AI revolution will benefit new players who learn quickly to use it to their advantage. AI will be a fundamental predictive enabler helping us solve large-scale problems, and startups are poised to gain a competitive edge.
So what’s the ground level AI sentiment of Startups? – Mix of Hope & Fear
Regardless of which industry you operate, be careful that AI will affect your world in some way. Look into what is present now and how you can utilize it to gain a competitive edge.
The possibilities with AI are endless; enterprises will become efficient, intelligent and cost-effective.
Undoubtedly, the digital revolution and AI will advance to a point where it will offer real-world benefits to every business- large and small.
Mark Zuckerberg says, “We’re working on AI because we think more intelligent services will be much more useful for you to use.”
AI is relevant because of its immense power to deliver useful solutions; its other building blocks including cloud computing and superfast connectivity. But, if you want to take advantage of this novel technology you will need a reliable, secure, and continuously evolving infrastructure.
Chatbots – The Protege of AI & Data Sciences
There has been a great deal of talk about the use of Artificial Intelligence chatbots in the last few weeks, especially given the news that Facebook are looking to implement AI into their Messenger and WhatsApp platforms, which are currently used by more than 1.8 billion people worldwide. However, does this bode well for the relationship between humans and Artificial Intelligence programs? Would you rather speak to an intelligent algorithm rather than a fellow human being?
The Sales and Customer Support Bot-ler ?
Chatbots, done right, are the cutting-edge form of interactive communications that captivate and engage users. But what kind of potential do they have for sales & customer support ?
To answer this, I should emphasize that customer service can be a delicate field. A lot of consumer engagement with a company happens when something goes wrong — such as a recently-purchased broken product or an incorrect bill or invoice.
By nature, these situations can be highly emotional. And as a business, you want to be responsive to potentially problematic customer inquiries like these. So relying on a chatbot to resolve issues that require a human touch might not be the best idea.
This is especially true if you let your bot “learn” from interactions it sees (say, in user forums) with no or minimal supervision. Things can easily go wrong, as the disaster around Microsoft’s Twitter bot “Tay” showed.
On the other hand, with the right supervision and enough training data, machine learning as an A.I. technique can help build very responsive and accurate informational chatbots — for example those that are meant to help surface data from large text collections, such as manuals.
I’d say that machine learning as a technique has been shown to work best on image processing. The advancements that Google, Facebook, and innovative startups such as Moodstocks (just acquired by Google) are showing in that space are truly amazing. Part of the amazement however, comes from the fact that we now see software take on another cognitive task that we thought could only be managed by humans.
What can bots do for the bottom line?
In my opinion, a bot’s primary application lies in customer service since most companies unfortunately continue to rely on an ancient methodology to manage customer interaction. And this is to be expected as most consumers themselves are still “hard-wired” to pick up a phone and dial a number when they want to engage with a company.
Companies haven’t necessarily made it easy for consumers to transition to digital-first interaction. Consumers are forced to either download a mobile app, browse websites, or use voice, the “dumbest” channel the smartphone has to offer, to retrieve information or perform transactions.
This is truly unfortunate because when it comes to paying a bill, checking on an order status, or reviewing account transactions, nothing is easier than sending a simple message. And with 900 million users now on Facebook Messenger, 1 billion on WhatsApp, and hundreds of millions more on basic SMS, companies have a consumer-preferred new medium for engaging with customers.
With messaging, a simple question can be posed in a simple message such as “Where is my order?”
Contrast this to the conventional options of being forced to shepherding that question through a maze of web or mobile app menus, or with IVR systems over the phone. Now imagine how a consumer-adopted, digital and automated interaction for simple questions vs. agent interaction over the phone could impact customer service and its cost. When chatbots handle the most commonly-asked questions, agent labor is reduced or redeployed to manage more complex and time-consuming interactions. Simple and moderate issues are resolved faster, leading to greater customer satisfaction and long-term loyalty. Bots can help deflect calls from the contact center and your IVR, which further reduces speech recognition license and telephony cost.
Could there be Bot-tle-necks?
There is also the question of whether these chatbots will take jobs from humans; a subject of fierce debate for all industries and levels in the last few months. Facebook itself has been quick to clarify that these chatbots are not going to replace the people in their organisation, but instead to work alongside them. For example, Facebook have said that the customer service executives will be required to train the AI bots, and to step in when the AI comes unstuck, which is likely to be fairly frequently in the early stages! Chinese messenger service WeChat has taken the chatbot idea on, with companies having official accounts through which they are able to communicate with their customers. However, the platform is still in its early stages, and is reported to be incredibly frustrating to use, so those in the customer service sector needn’t worry that their jobs are under threat quite yet!
While we might see chatbots starting to appear through the likes of Facebook Messenger and WhatsApp platforms in the coming 12 months, and will be dedicating teams of engineers to train the platforms, rather than relying on the general public. There are three main factors on which their success depends.
The first is with how much freedom AI in general is allowed to be developed, especially given the hesitation that the likes of Elon Musk and Bill Gates have about a potential ‘Singularity’, with Musk recently being quoted as saying that ‘Artificial Intelligence is our biggest existential threat’.
The second is arguably more important; how willing the general public are to help develop the chatbots, by having conversations with them, in the knowledge that they are talking to an autonomous entity.
More important still, are these chatbots going to be safe from cyberattacks? How will you know if your financial information will be secure if you disclose it to a chatbot, especially if there are unlikely to be the same multi-stage security checks that are the hallmark of P2P customer service interactions?
The Road Ahead?
Many companies are already launching bots for customer acquisition or customer service. We will see failures, and in parts, have already seen some. Bots are not trivial to build: you need people with experience in man-machine interface design. But to quote Amara’s Law: “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.”
Bots are here to stay, and will be a great new platform and make things easier for all of us. But bots that try to do too much or set unreasonable expectations will slow consumer confidence and acceptance of them. What might help us now is maybe to calm down a bit with the hype, and focus on building good bots that have value — then share our experiences, and show the world where the true value lies.