Conversational AI & Banking

Conversational AI & Banking

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There has been a steady increase in the standard of customer service in the range of capabilities that automated systems offer. We’re now in an era of AI that can talk and is poised to change the way people interact with financial institutions and do their banking. With seamless omnichannel experiences becoming increasingly crucial for today’s consumers, banks need tools that allow them to deliver consistent messaging across touchpoints to keep their customers engaged. Let’s take a look at six specific benefits of conversational AI in banking.

Benefits of AI in Banking

#1: Customer Self-Service & Guidance

The use of AI in conversation is predicted to provide customer self-service and guidance. We are witnessing a technological breakthrough that allows customers to receive the required information without having to go through multiple support representatives. This is practically true of contextual banking which allows customers to receive personalized financial guidance based on their specific situation.

Conversational AI could aid back-office employees to become more productive by providing them with information and insight in real-time. As chatbots and conversational AI systems are relatively new and not yet tested by banks, they will require high-end security standards before they can be integrated into production environments.

#2: Contextual Banking

Contextual banking helps a client with accurate information at the appropriate time. It offers customers individualized services and advice both inside and outside of their branch at the bank. Contextual banking is an essential element for self-service and guidance, as well as other services such as personal financial advice and secure payment transactions.

To provide contextual banking services, banks require real-time access to customer data and information to offer relevant products and services. AI technology has been an important factor in this trend by enabling banks to create actionable and scalable analytics capabilities.

#3: Personalized Financial Advice

Personalized financial guidance is a method banks can use to enhance the customer experience. It uses conversational AI to give customers real-time personalized information regarding their financial situation. The personalized financial advice you receive is available in two different ways:

Customer service: The initial and most obvious method for customized financial advice is to assist customers with their daily banking requirements. This can include checking balances, moving money, paying bills, or paying off loans.

Customer experience: Personalized financial advice can increase your interaction with your bank via any channel (online or on mobile). For instance, if you’re seeking student loans, your bank may notify you via email or text message to inform you of new programs that could benefit you, depending on your age and location.

#4: Back-Office Productivity

Back-office productivity is a well-known and widely understood benefit of using conversational AI chatbots. Through natural machine learning and processing of language, banks can automatize manual tasks, reduce costs, improve efficiency, and enhance customer service. Conversational AI chatbots automate repetitive procedures that are prone to error by humans. They also enhance the quality of data by reducing omissions or typos when performing important processes like opening accounts or applying for loans.

Conversational AI chatbots in banking can improve transaction processing speed by entering all of them at one time. This helps to avoid mistakes caused by fatigue or human error. It Improves accuracy by removing mistakes that occur when several people perform the same job, such as sending invoices manually across the various departments of an organization.

#5: Enterprise-Grade Security

AI is an important tool to protect your data. It helps detect cyber attacks and fraud, identify and reduce security vulnerabilities, ensure you are in compliance with laws such as GDPR, and carry out security tasks. Enterprises employ these tools to protect their networks and comply with regulatory requirements. An AI solution could increase the effectiveness of these programs by eliminating manual work from the process while decreasing costs.

#6: Omnichannel Banking Experience

The experience of the customer is an important factor in the market. The customer is becoming more demanding and is presented with a variety of choices. Their experiences are multi-faceted and multi-channel, personal, and relevant. The omnichannel banking experience ensures that customers can seamlessly interact with their bank through all channels. They can also communicate in real-time, meaning they don’t have to wait for a response or use multiple channels to obtain the required information.

A Tool for the Next Generation

With conversational AI, you will no longer have to navigate complicated menus or wait in line. Instead, your bank will not have restrictions on its offerings and will be able to provide specific financial advice and contextualized banking services tailored to every customer. With the implementation of conversational AI, customers will spend more time communicating with brands than ever before. And more importantly, these interactions will be personal. The future for banking is now here. It is not a matter of when, but how you will use AI to your advantage.

Digital Transformation & Automation Operating Systems

Most enterprises are having a difficult time definitively achieving full-blown digital transformation. Simultaneously, the entire software industry is also struggling to figure out how to best speed up the transformation. The reasons why digital transformation projects fail are complex enough to grasp. One explanation may lie in the approach adopted by the tech industry.

Digital Transformation: The Intrinsic Problem

As humans, we know what we want to achieve. Our brains tell us anything is doable. But when faced with building the ultimate solution in software and where we need to cope with the heterogeneity of things very quickly, we run into quicksand very fast.?

The subsequent cost, time, and resource loss, not to mention the impact on organizational confidence, is simply huge. For example, it’s not atypical to see hundreds of millions spent on digital transformation projects only to flounder. According to TechTarget, $1 trillion had been invested in digital transformation projects with the Boston Consulting Group estimating at least 70 percent of these initiatives fell far short of their original goals by March 2021. The problem has become so apparent that it is not unusual for a CEO to dodge analysts’ questions in earnings calls on Digital Transformation.

So, what is the intrinsic problem? Humans with a passion for building tend to also love three things:

Complexity

Originality

No clock ticking in the background.

However, maybe the more important question is rather than building software that fails to deal with the complexity of things fundamentally, why not take a shortcut and focus on the integration of things?

I started my career in the software industry, building network management systems in the 1990s, followed by cloud software and IoT platforms, and so on. I always accepted that my tried and trusted engineering colleagues always knew best. In all fairness, the engineers did know best, but they struggled when challenged to ponder the sheer complexity of the computer science challenges they were facing and how things were becoming exponentially complex.?

Then, I really started to get very curious about ways to solve the mountainous challenges being faced in digital transformation. Not only given the great quantum leap in the complexity of business logic modeling required, but also a new methodology in software that could mirror the aspirations of a human’s creative mind into solutions that would actually work.

So with that goal in mind, I started looking at automation engines to solve digital transformation problems.

The Future of Digital Transformation

In the 1980s and ’90s, computing took a big shift forward with the introduction of operating systems (OS). Therefore, why not look for an automation engine that functions like an OS? One where its role is to orchestrate software workflows from disparate data sources in real-time in unison and is built on the basis of the user/designers’ needs, namely low-code and even better no code.

An easy-to-use, visual canvas connecting to pre-existing “any-to-any” applications/systems of record automating mission-critical workflows and speeding up actions via an ultra-fast rules engine may be necessary. For the data scientists, a real-time automation OS that more specifically enables deployment of low-code orchestration from any system and device or data record to any other system can result in an incredible end customer experience. Many industry commentators believe there has been a missing link between operations and the massive investment in AI. I also believe in automation.

Today, the automation industry is in full flight robotic process automation (RPA) software companies where “hyper-automation” is the new buzzword. An entirely new generation of automation technologies is appearing in the market with RPA being the most common for solving simpler digital transformation problems.

Reaping the Benefits of Automation

As the world braces for a very tough economic backdrop, the biggest win to focus on is the enormous savings that will be reaped from automation at a time of record software skills shortages globally. Other target metrics to look for from an Automation OS are 10x faster time to market, 100x less code, 15x less development time, and up to 20x operating expenses (OPEX) and capital expenditures (CAPEX) cost savings. And finally, break-even ROI within nine months or less.

What is an IoT Platform?

IoT platforms provide a head start in building IoT systems by providing built-in tools and capabilities to make IoT easier and cheaper for businesses, developers, and users.

IoT platforms are a vital component of the IoT ecosystem and a fast-growing market, expected to exceed $22 billion by 2023. IoT platforms provide a huge amount of value to businesses – allowing them to lower development costs, accelerate launch and streamline processes. However, many people are still unclear on what an IoT platform is exactly, what they do, and when a business should use one.

In this piece, I’ll provide a simple, non-technical explanation of IoT platforms. What they are, why there are so many, when businesses should use them, and the important considerations when choosing between the many options.

So what is an IoT Platform exactly?

To understand what an IoT platform is, you first need to understand what goes into a complete IoT system. My previous post, How Does an IoT System Actually Work?, is a great way to learn, but I’ll summarize it for you quickly:

A complete IoT system needs hardware, such as sensors or devices. These sensors and devices collect data from the environment (e.g. a moisture sensor) or perform actions in the environment (e.g. watering crops).

A complete IoT system needs connectivity. The hardware needs a way to transmit all that data to the cloud (e.g. sending moisture data) or needs a way to receive commands from the cloud (e.g. water the crops now). This can be accomplished with mature forms of connectivity like cellular, satellite, or WiFi, or may necessitate more recent, IoT-focused connectivity options like LoRa.

A complete IoT system needs software. This software is hosted in the cloud (what’s the cloud?) and is responsible for analyzing the data it’s collecting from the sensors and making decisions (e.g knowing from moisture data that it just rained and then telling the irrigation system not to turn on today).

Finally, a complete IoT system needs a user interface. To make all of this useful, there needs to be a way for users to interact with the IoT system (e.g a web-based app with a dashboard that shows moisture trends and allows users to manually turn irrigation systems on or off).

In addition, the true value of IoT is unlocked when integrated with existing business systems and data streams. It’s therefore critical that all of these disparate components get tied together effectively and in a manageable way.

At a high level, IoT platforms provide a head start in building IoT systems by providing built-in tools and capabilities to make IoT easier and cheaper for businesses, developers, and users. An IoT platform helps facilitate the communication, data flow, device management, and the functionality of applications.

IoT platforms exist in part 3 and, often, part 4 of what’s described above. With all the varying kinds of hardware and the different connectivity options, there needs to be a way of making everything work together. IoT platforms help solve that problem.

IoT platforms help:

Connect hardware, such as sensors and devices

Handle different hardware and software communication protocols

Provide security and authentication for devices and users

Collect, visualize, and analyze data the sensors and devices gather

Integrate all of the above with existing business systems and other web services

Why are there so many IoT platforms?

Platforms are not unique to IoT, but if you look at other domains you’ll notice that there are many fewer platform options. Android and iOS are two dominant mobile platforms, Windows and MacOS desktop platforms, and Xbox and Playstation gaming console platforms. If all of these markets have just a few dominant players, why don’t we see the same in IoT?

Some may make the argument that we already have the dominant players in Amazon (AWS IoT Core), Microsoft (Azure IoT Hub), and Google (Google IoT Core). However, these platforms are more focused on the infrastructure level and therefore require greater expertise and customization to build specific IoT applications for businesses. IoT platforms are often built on top of these infrastructure providers, offering additional tools and services to quickly build IoT applications for businesses.

The question of why there are so many IoT platforms might be answered by 1) that the market is still so nascent that dominant players have yet to emerge, or 2) that because of the infinite IoT applications across industries, there will be different platforms focused on different breeds of applications. As always, it’s probably a mix of the two, but I believe that the first explanation is more compelling. This makes it all the more important to consider which one you use because if you choose an IoT platform that doesn’t become one of the dominant players, this could prove problematic in the future.

When should your business use an IoT platform?

Because IoT is a system of systems—a network of devices and software applications—rarely does an organization have expertise across all the relevant domains. Since IoT depends upon the correct synthesis of engineering fields as distinct as mechanical, electrical, software (to name a few), IoT platforms come to the rescue to help businesses overcome technical challenges without needing to salary and manage teams of engineers specializing in the various fields IoT requires, when only a single project or two needs to be developed.

For example, your business might be really good at building hardware and decide that you want to make your hardware “smart.” Instead of the expensive and time-intensive process of hiring software developers to build everything in-house, you can instead use an IoT platform to get up and running quickly and more cost-effectively.

However, there is a tradeoff. IoT platforms that save you time may cost more, in the long run, depending on how they’re priced. This is because they charge use-based and/or subscription fees that can add up over time. But you still get the benefit of significantly lower up-front costs (no CapEx).

IoT platforms that are inexpensive upfront will likely cost you more in time/effort. This comes back to the same point in bold above, the less you spend the more work you’ll have to do on your own, which takes time.

What is the Internet of Things, or IoT? A Simple Explanation.

The Internet of Things, or "IoT" for short, is about extending the power of the internet beyond computers and smartphones to a whole range of other things, processes, and environments. Here's everything you need to know.

The Internet of Things (IoT) has been growing over the last several years, and predictions are showing that it’s going to grow even faster in the years to come. It’s at the center of technological advances in every industry imaginable. This may seem like a bit of a hyperbole, but it’s unlikely we go a day without encountering IoT in some way. What is IoT, though? The Internet of Things continues to expand, but this is everything you need to know about the Internet of Things the next time you see it pop up in the news.

What is IoT?

If you just Google “What is IoT?” many of the answers are unnecessarily technical. Case in point:

“The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction.”

—An unnecessarily technical explanation of IoT

You’re not alone if you’re confused. Most people neither want nor need to dive into the nitty-gritty of IoT. In this post, I’ll provide you with a simple explanation of the Internet of Things and how it works.

Before we jump in, note that “The Internet of Things” and “IoT” can and will be used interchangeably.

IoT Explained: Simple and Non-Technical

You might be reading this on desktop or tablet, but whatever device you’re using, it’s connected to the internet.

Connecting things to the internet yields many amazing benefits. We’ve all seen these benefits with our smartphones, laptops, and tablets, but this is true for everything else too. And yes, I do mean everything.

The Internet of Things means taking all the things in the world and connecting them to the internet.

I think the confusion arises not because the concept is so narrow, but rather because it’s so broad and loosely defined. It can be hard to nail down the concept when there are so many examples and possibilities in IoT.

To help clarify, I think it’s important to understand the benefits of connecting things to the internet.

Why IoT Matters

When something is connected to the internet, that means that it can send information or receive information, or both. This ability to send and/or receive information makes things smart, and smarter is better.

Let’s use smartphones again as an example. You can listen to any song in the world, but not because your phone has every song stored on it. It’s because every song in the world is stored somewhere else (that place is known as “the cloud”), and your phone can request a song, and receive information to stream it.

To be smart, a thing doesn’t need to have super storage or a supercomputer inside of it. All a thing has to do is connect to super storage or to a supercomputer. Being connected is awesome.

In the Internet of Things, all the things can be put into three categories:

Sensors that collect information and then send it.

Computers that receive information and then act on it.

Things that do both.

And all three of these have enormous benefits that feed on each other.

Collecting and Sending Information

This means sensors. Sensors can measure temperature, motion, moisture, air quality, light, and almost anything else you can think of. Sensors, when paired with an internet connection, allow us to collect information from the environment which, in turn, helps make better decisions.

On a farm, automatically getting information about soil moisture can tell farmers exactly when crops need to be watered. Instead of watering too much or too little (either of which can lead to bad outcomes), the farmer can ensure that crops get exactly the right amount of water.

Just as our senses allow us to collect information, sensors allow machines to make sense of their environments.

Receiving and Acting on Information

We’re all very familiar with machines acting on input information. A printer receives a document and then prints it. A garage door receives a wireless signal and the door opens. It’s commonplace to remotely command a machine to act.

So what? The real power of IoT arises when things can both collect information act on it.

Doing Both

Let’s go back to farming. The sensors collect information about soil moisture. Now, the farmer could activate the irrigation system, or turn it off as appropriate. With IoT-enabled systems, you don’t actually need the farmer for that process. Instead, the irrigation system can automatically act as needed, based on how much moisture is detected.

You can take it a step further too. If the irrigation system receives information about the weather from its internet connection, it can also know when it’s going to rain and decide not to water the crops when they’ll be watered by the rain anyways.

And it doesn’t stop there! All this information about the soil moisture, how much the irrigation system is watering the crops, and how well the crops actually grow can be collected and sent to supercomputers in the cloud that run algorithms to that analyze all this information, leading to models that could be used to predict future conditions and prevent losses.

And that’s just one kind of sensor. Add in other sensors like light, air quality, and temperature, and these algorithms can learn much much more. With dozens, hundreds, thousands of farms all collecting information, these algorithms can create incredible insights into how to make crops grow the best, helping to feed the world’s growing population.

Your Takeaway Definition of IoT

What is IoT?: The Internet of Things, or IoT, is about extending the power of internet connectivity beyond computers to a whole range of other things, processes, and environments. Those connected, smarter, things are used to gather information, send information, or both.

Why does IoT matter?: IoT provides businesses and people better insight into and control over objects and environments that are currently beyond the reach of the internet. By doing so, IoT helps businesses and people to be more connected to the world around them and to do more meaningful, higher-level work.

The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning

Explanations of Artificial Intelligence, Machine Learning, and Deep Learning and how they’re all different. Plus, how AI and IoT are inextricably connected.

We’re all familiar with the term “Artificial Intelligence.” After all, it’s been a popular focus in movies such as The Terminator, The Matrix, and Ex Machina (a personal favorite of mine). But you may have recently been hearing about other terms like “Machine Learning” and “Deep Learning,” sometimes used interchangeably with artificial intelligence. As a result, the difference between artificial intelligence, machine learning, and deep learning can be very unclear.

I’ll begin by giving a quick explanation of what Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) actually mean and how they’re different. Then, I’ll share how AI and the Internet of Things are inextricably intertwined, with several technological advances all converging at once to set the foundation for an AI and IoT explosion.

So what’s the difference between AI, ML, and DL?

First coined in 1956 by John McCarthy, AI involves machines that can perform tasks that are characteristic of human intelligence. While this is rather general, it includes things like planning, understanding language, recognizing objects and sounds, learning, and problem-solving.

We can put AI in two categories, general and narrow. General AI would have all of the characteristics of human intelligence, including the capacities mentioned above. Narrow AI exhibits some facet(s) of human intelligence, and can do that facet extremely well, but is lacking in other areas. A machine that’s great at recognizing images, but nothing else, would be an example of narrow AI.

At its core, machine learning is simply a way of achieving AI.

Arthur Samuel coined the phrase not too long after AI, in 1959, defining it as, “the ability to learn without being explicitly programmed.” You see, you can get AI without using machine learning, but this would require building millions of lines of codes with complex rules and decision-trees.

So instead of hard coding software routines with specific instructions to accomplish a particular task, machine learning is a way of “training” an algorithm so that it can learn how. “Training” involves feeding huge amounts of data to the algorithm and allowing the algorithm to adjust itself and improve.

To give an example, machine learning has been used to make drastic improvements to computer vision (the ability of a machine to recognize an object in an image or video). You gather hundreds of thousands or even millions of pictures and then have humans tag them. For example, the humans might tag pictures that have a cat in them versus those that do not. Then, the algorithm tries to build a model that can accurately tag a picture as containing a cat or not as well as a human. Once the accuracy level is high enough, the machine has now “learned” what a cat looks like.

Deep learning is one of many approaches to machine learning. Other approaches include decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks, among others.

Deep learning was inspired by the structure and function of the brain, namely the interconnecting of many neurons. Artificial Neural Networks (ANNs) are algorithms that mimic the biological structure of the brain.

In ANNs, there are “neurons” which have discrete layers and connections to other “neurons”. Each layer picks out a specific feature to learn, such as curves/edges in image recognition. It’s this layering that gives deep learning its name, depth is created by using multiple layers as opposed to a single layer.

AI and IoT are Inextricably Intertwined

I think of the relationship between AI and IoT much like the relationship between the human brain and body.

Our bodies collect sensory input such as sight, sound, and touch. Our brains take that data and makes sense of it, turning light into recognizable objects and turning sounds into understandable speech. Our brains then make decisions, sending signals back out to the body to command movements like picking up an object or speaking.

All of the connected sensors that make up the Internet of Things are like our bodies, they provide the raw data of what’s going on in the world. Artificial intelligence is like our brain, making sense of that data and deciding what actions to perform. And the connected devices of IoT are again like our bodies, carrying out physical actions or communicating to others.

Unleashing Each Other’s Potential

The value and the promises of both AI and IoT are being realized because of the other.

Machine learning and deep learning have led to huge leaps for AI in recent years. As mentioned above, machine learning and deep learning require massive amounts of data to work, and this data is being collected by the billions of sensors that are continuing to come online in the Internet of Things. IoT makes better AI.

Improving AI will also drive adoption of the Internet of Things, creating a virtuous cycle in which both areas will accelerate drastically. That’s because AI makes IoT useful.

On the industrial side, AI can be applied to predict when machines will need maintenance or analyze manufacturing processes to make big efficiency gains, saving millions of dollars.

On the consumer side, rather than having to adapt to technology, technology can adapt to us. Instead of clicking, typing, and searching, we can simply ask a machine for what we need. We might ask for information like the weather or for an action like preparing the house for bedtime (turning down the thermostat, locking the doors, turning off the lights, etc.).

Converging Technological Advancements Have Made this Possible

Shrinking computer chips and improved manufacturing techniques means cheaper, more powerful sensors.

Quickly improving battery technology means those sensors can last for years without needing to be connected to a power source.

Wireless connectivity, driven by the advent of smartphones, means that data can be sent in high volume at cheap rates, allowing all those sensors to send data to the cloud.

And the birth of the cloud has allowed for virtually unlimited storage of that data and virtually infinite computational ability to process it.

Of course, there are one or two concerns about the impact of AI on our society and our future. But as advancements and adoption of both AI and IoT continue to accelerate, one thing is certain; the impact is going to be profound.

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