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AI today can be described in terms of three application domains: cognitive automation, cognitive engagement and cognitive insight.
- Cognitive automation: In the first AI domain are machine learning (ML), Robotics Process Automation (RPA), natural language processing (NLP) and other cognitive tools to develop deep domain-specific expertise and then automate related tasks.
- Cognitive engagement: At the next level of the AI value tree lies cognitive ‘agents’: systems that employ cognitive technology to engage with people, unlocking the power of unstructured data (industry reports / financial news) leveraging text/image/video understanding, offering a personalized engagement between banks and customers with personalized product offerings and unlocking new revenue streams.
- Cognitive insights: Cognitive Insights refer to the extraction of concepts and relationships from various data streams to generate personalized and relevant answers hidden within a mass of structured and unstructured data. Cognitive Insights allow to detect real time key patterns and relationships from large amount of data across multiple sources to derive deep and actionable insights.
Here are five key applications of artificial intelligence in the Banking industry that will revolutionize the industry in the next 5 years.
AML Pattern Detection
Anti-money laundering (AML) refers to a set of procedures, laws or regulations designed to stop the practice of generating income through illegal actions. In most cases, money launderers hide their actions through a series of steps that make it look like money that came from illegal or unethical sources are earned legitimately.
HSBC has partnered with Silicon Valley-based artificial intelligence startup Ayasdi to automate some of its compliance processes in a bid to become more efficient. The banking group is implementing the company’s AI technology to automate anti money-laundering investigations that have traditionally been conducted by thousands of humans, the bank’s Chief Operating Officer Andy Maguire said in an interview last week.
Chat bots are already being extensively used in the banking industry to revolutionize the customer relationship management at personal level. Bank of America plans to provide customers with a virtual assistant named “Erica” who will use artificial intelligence to make suggestions over mobile phones for improving their financial affairs. Allo, released by Google is another generic realization of chat bots.
The State Bank of India (SBI) on Monday announced SBI Intelligent Assistant (SIA) — a chat assistant aimed to address customer enquiries like a “bank representative” does. Developed by Payjo, an artificial intelligence (AI) banking platform, “SIA” is equipped to handle nearly 10,000 enquiries per second or 864 million in a day — which is nearly 25 per cent of the queries processed by Google each day.
Plenty of Hedge funds across the globe are using high end systems to deploy artificial intelligence models which learn by taking input from several sources of variation in financial markets and sentiments about the entity to make investment decisions on the fly. Reports claim that more than 70% of the trading today is carried out by automated artificial intelligence systems. Most of these hedge funds follow different strategies for making high frequency trades (HFTs) as soon as they identify a trading opportunity based on the inputs.
A few hedge funds active in AI space are: Two Sigma, PDT Partners, DE Shaw, Winton Capital Management, Ketchum Trading, LLC, Citadel, Voleon, Vatic Labs, Cubist, Point72, Man AHL.
Fraud detection is one of the fields which has received massive boost in providing accurate and superior results with the intervention of artificial intelligence. It’s one of the key areas in banking sector where artificial intelligence systems have excelled the most. Starting from the early example of successful implementation of data analysis techniques in the banking industry is the FICO Falcon fraud assessment system, which is based on a neural network shell to deployment of sophisticated deep learning based artificial intelligence systems today, fraud detection has come a long way and is expected to further grow in coming years.
Mastercard announced the acquisition of Brighterion. Brighterion’s portfolio of AI and machine learning technologies provide real-time intelligence from all data sources regardless of type, complexity and volume. Its smart agent technology will be added to Mastercard’s suite of security products already using AI.
Recommendation engines are a key contribution of artificial intelligence in banking sector. It is based on using the data from the past about users and/ or various offerings from a bank like credit card plans, investment strategies, funds, etc. to make the most appropriate recommendation to the user based on their preferences and the users’ history. Recommendation engines have been very successful and a key component in revenue growth accomplished by major banks in recent times.
With Big Data and faster computations, machines coupled with accurate artificial intelligence algorithms are set to play a major role in how recommendations are made in banking sector. For further reading on recommendation engines, you can refer to the complete guide of how recommendation engines work.
JPMorgan, which is spending big on technology as it looks to cut costs and increase efficiency, last year launched a predictive recommendation engine to identify those clients which should issue or sell equity. And now, given the initial success of the engine, it’s being rolled out to other areas.
Strategic Challenges of AI
As with any new endeavor, there are several challenges associated with the development and application of AI solutions.
- Most banks and credit unions are in the early stages of adopting AI technologies. According to a survey conducted by Narrative Science in conjunction with the National Business Research Institute, 32% of financial services executives surveyed confirmed using AI technologies such as predictive analytics, recommendation engines, voice recognition and response.
- Also, one of the biggest challenges is finding the right talent. With only slightly more than half of survey respondents (55%) stating they have identified an AI leader within their company, more than half of those have appointed the head of innovation as the leader.
- In some cases, current employees will not be well positioned for the ‘new age of banking.’ In other cases, the transformation of labor caused by the advances of AI will eliminate some positions entirely.
- 12% of the overall group weren’t using AI yet because they felt it was too new, untested or weren’t sure about the security.
- There is no clear internal ownership of testing emerging technologies— only 6% of those surveyed having an innovation leader or an executive dedicated to testing new ideas and processes.
How to make AI Part of Banking Ecosystem
The potential of open banking and artificial intelligence are intertwined, making up the foundation for a new banking ecosystem that will most likely include both financial and non-financial components. By partnering with fintech providers and data analytic professionals, the power of organizational data and insights can be realized. The partnerships and structure decided upon today will determine an organization’s competitive differentiation in the future.
Multiple providers are offering AI-based solutions and, as a result, banks need to navigate between specialist players and AI powerhouses. The goal will not to become more automated and less personalized, but to use technology and customer insights to become a lot more personalized and contextual.
The banking industry is still in the early stages of developing strong AI solutions. While these solutions can impact the cost and revenue structures of financial organizations, the real potential is with how artificial intelligence can improve the customer experience. Singaporean bank DBS had the vision to launch Digi bank, India’s first mobile-only bank. Being paperless and branchless, Digi bank had to rely on emerging technologies like conversational AI to succeed. Digi bank was built with one-fifth of the cost of a regular retail bank and can contain 82% of customer inquiries with bots. Some banks just want to hand off responsibility to the vendor but Digi bank’s approach is to empower the customer with self-service tools. They don’t want to be professional services
There are four key recommendations that experts make to financial services firms who are looking to effectively exploit the value of AI. These are:
- Look to invest, learn and pair up with experts from outside of the industry
- Make use of cognitive computing to make better use of data
- Implement the right mix of platform technologies
- Strive to maintain a human touch.
In conclusion, it is evident that AI is here to stay, and is impacting a large number of industries, Banking is an early adopter of this trend. This trend is likely to grow exponentially in the future. Companies that embrace this trend are likely to be winners
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There seems to be a glaring ambiguity as to exactly what artificial intelligence (AI) is, and how the discipline of AI should be categorized. Is AI a form of analytics or is it a totally new discipline that is distinct from analytics? I firmly believe that AI is more closely related to predictive analytics and data sciences than to any other discipline. One might even argue that AI is the next generation of predictive analytics and is born out of sophistication of analytics . Additionally, AI is often utilized in situations where it is necessary to operationalize the analytics process. So, in that sense, AI is also often pushing the envelope of prescriptive, operationalized analytics. It would be a mistake to say that AI is not a form of analytics.
I’ve seen AI applied to some of the most obscure topics you can imagine, ranging from industrial energy usage all the way to finding the right GIFs. Using Artificial Intelligence to improve and create solutions to today’s pressing business and social problems is one of the defining trends of the tech world for me.
So, if you are a PE / VC entity and are looking for investment opportunity in AI space, you will have to understand what kinds of AI companies exist and how this AI practice has evolved from Analytics practice.
There are three types of AI companies — core, applied, and industry
1. Core AI Companies
Core AI companies develop technology that improves parts of the AI creation or deployment process itself. Here are a few selected parts of that process and a few companies that are innovating in each:
Data scrubbing and cleaning: Trifacta, Paxata, Wealthport, Datalogue
Modeling: Sentient, Petuum, MLJar
Deployment: Yhat, Seldon
These companies all innovate in some specific, industry-agnostic part of the AI pipeline. Some of them are specific tools, while others purport to have an entirely new approach to AI that will revolutionize how it’s done (see Geometric Intelligence circa 2015).
If you’re investing in Core AI companies, you should probably have a good understanding of how this pipeline works. If you’re founding one of these companies, you should probably have experience deploying Machine Learning and AI at scale.
2. Applied AI Companies
A bit on the more specific side, Applied AI start-ups develop technology that helps companies across different industries perform a specific task using AI. As with the above, here are some examples of those applications and a few interesting companies in each:
Analysing and understanding text: Indico, Synapsify, Lexalytics
Analysing and understanding images and videos: Clarifai, Kairos, Imagry, Affectiva, Deepomatic
Bots / Voice: Init.ai, MindMeld
While investors can get away with not having experience in one of these specific applications, founders will likely have done projects involving this stuff in the past.
The implementation of AI in this scenario corresponds to the implementation of predictive analytics. At its core, predictive analytics is, naturally, about predicting something. Who will buy? Will certain equipment break? Which price will maximize profits? Each of these questions can be addressed by following a familiar workflow – First, we identify a metric or state that we want to predict and gather historical information on that metric or state. Next, we gather additional data that we believe could be relevant to predicting our target. Then, we pass the data through one or more algorithms that attempt to find a relationship between the target and the additional data. Through this process, a model is created that produces a prediction if new data is fed to it. If a customer had this profile, how likely would she be to respond? If we priced at this point, how much profit might we expect?
The goals and steps followed within an AI process are the same. Let’s look at two examples:
Take image recognition. First, we identify a bunch of cat pictures. Then, we grab a bunch of non-cat pictures. We pass a deep learning algorithm over the images to learn to accurately predict whether an image is a cat. When provided with a new image, the model will answer with the probability that the image is a cat. Sounds a lot like predictive analytics, doesn’t it?
Let’s now consider natural language processing (NLP). We gather a wide range of statements that have specific meanings we care about. We also gather a wide range of other statements. We run NLP procedures against the data to try to tease out how to tell what is important and how to tell what is being asked. As we feed a new line of text to the process, it will identify what the point of the statement is in probabilistic terms. The NLP process will assign probabilities to various possible interpretations and send those back (think Watson playing jeopardy). This also sounds a lot like predictive analytics.
3. Industry AI Companies
The final category of ML/AI companies apply these techniques to specific business problems in specific verticals. This is undoubtedly the lion’s share of the actual number of companies being founded, and in many ways, represents the true promise of AI — solving actual and immediate problems with new techniques. Here, it’s easier to give companies as examples. The format is always “AI for ________”:
DigitalGenius: AI for customer support
Cylance: AI for cyber threat prevention
X.ai: AI for scheduling meetings
Drive.ai: AI for autonomous vehicles
The implementation of AI in this scenario corresponds to industrialized embedded analytics. A major trend today is to embed analytics into business applications so that the models are utilized in an automated, embedded, prescriptive fashion at the point of a business decision. For example, as a person navigates a web page, models are utilized to predict what offers should appear on the next page. There is no human intervention once the process is in place. The process makes offers until told to stop.
Many applications of AI today also require industrialization. For example, as an image is posted on social media, it is immediately analysed to identify who is present in the image. As I make a statement to Siri or Alexa, it attempts to determine what I said and what the best answer is. While this qualifies as a more advanced application of predictive analytics that moves into embedded, prescriptive, automated processes, it is still very much in line with how industrialized embedded analytics are being used today.
The common theme among these companies is that they take Machine Learning / AI and use it on a specific problem or space. When researching investments like this, investors should look at both the AI itself (if it works well) and the business case (whether it’s compelling). In x.ai’s case, investors need to know if the AI works, but they should also consider whether AI is the best way to solve the scheduling problem, and whether scheduling is a problem worth solving at all. With the other two types of companies, this is rarely a consideration. Founders of these types of companies can often not have AI experience and can even be non-technical (with the right supporting team and CTO, of course).
Your journey to a fruitful AI investment will be far easier if you recognize and embrace AI as sophistication of analytics and understand the true categorization, and then task your analysts with leading the charge. Don’t cause confusion and redundancy by considering AI to be something completely different.
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Previous week , I had an opportunity to moderate a fireside chat at NASSCOM Martech conference that carried a theme around changing role for CMO with the advent of AI and I could notice a substantial set of queries during the conference on how AI will redefine marketing. As understandable, each new technology can create fear, uncertainty, and doubt until we understand it better. And AI, with all its hype, fits that bill. But to remain current and relevant, CMOs must quickly understand and apply AI. Here’s a short AI CMO Primer.
Can I put off AI until later?
The answer is no! AI is here. Waiting to deal with it could put you well behind the curve. Leading businesses are already either using AI to profound effect, or actively planning for it.
- Amazon, the company that wants to eat everyone’s lunch, is already driving a third of its business from a AI-powered function: its recommended purchases.
- In a June 2016 report, Weber Shandwick found that 68% of CMOs report their company is “planning for business in the AI era” with 55% of CMOs expecting AI to have a “greater impact on marketing and communications than social media ever had.”
To wait is to get left behind. And as you’ll see later, getting started doesn’t have to be painful or costly.
What is AI, machine learning, and cognitive intelligence?
Academic experts might hate my explanation, but differentiating between AI, machine learning, and cognitive intelligence from a practical CMO perspective isn’t necessary. I use AI as an umbrella term to refers to software that carries out a task which normally requires human intuition—including learning and problem solving.
AI can be thought of as a set of repeatable steps and, while AI doesn’t technically replicate free-will and decision making, it does map out these steps and use computer processing speed to make its way through them to come to an outcome—like how a person would. It can do this much faster, and taking into account far more relevant data than a human would.
Is AI ready for marketing now?
AI has come at the right time, along with the explosion of Big Data. In essence, with access to an incredible amount of data, it’s never been more important for organizations to make sense of it and leverage important pieces out of the noise.
With the exponential growth of cheap, fast, scalable, and interconnected computing and storage in the cloud, the horsepower and data to efficiently run AI algorithms is now within everyone’s reach.
But, that being said, it is also sadly true that there’s one very simple reason why progress towards full automation and AI marketing is relatively sluggish – because most machines aren’t actually learning anything. All of these platforms that exist today, there’s no machine learning. And if it is, their machine learning is, ‘Did someone open an email? Yes, give them a point. That’s not real machine learning. Which is a problem, because effective automation is fast becoming a prerequisite of effective marketing. From chatbots to real-time contextual geographic marketing, modern marketing solutions demand insight-driven automation to deploy the right message quickly, at scale.
marketing automation (especially AI marketing) will have to eventually free marketers from manual work which comprises ‘98% of their eight hours a day’, empowering them to spend their time more productively tackling the creative jobs that machines aren’t well suited to. This requires three key problems AI marketing providers need to solve:
1. The creation of effective, scalable machine learning which can optimize a campaign without human input.
2. Ensuring that decision-making system’s logic is transparent and easily comprehensible by marketers seeking to analyze and augment those automated insights.
3. Designing a prescriptive system which can not only predict future actions – but understand why the user would make those actions.
How can AI be applied to marketing?
AI has the potential to revolutionize customer engagement, customer service, and marketing automation. It can enhance the way we communicate with new, current, and inactive customers, and automate admin functions at the backend. In other words, it can help make marketing operations more efficient and effective.
AI can far more accurately predict next best action, by churning through (in real-time) all relevant data about the customers – purchases, interactions, social media posts, email exchanges – and then learn from the results and do it on a scale not previously possible.
For example, let’s say you have a few million customers and want to communicate with them as if you know them very well, providing everyone the right offer at the right time. AI can enable this level of personalization at a scale of millions of individuals, and in near real time.
In essence, AI can save marketers time and bring companies far closer to their customers, without worrying about IT, data lakes, data quality, or hiring armies of data scientists.
Do I need to become an AI expert?
The short answer is no. AI systems shouldn’t require you to become a mathematician. With AI system, you’ll be able to focus on the results not the process of churning through of thousands, millions, or trillions of data points to arrive at the insights you need about your customers.
How much will it cost?
Surprisingly, AI systems can reduce costs and eliminate waste. AI systems can significantly reduce the requirement for data engineers and data scientists, or the need to depend on IT teams.
And AI can take wasted effort out of the system by providing a deeper understanding of what your customers want and how to interact with them effectively.
How do I get started?
First, start exploring today. Read, talk to people, and evaluate first hand. Select a contained, but impactful area business problem. A subset of your customer loyalty system could make a great initial project. Loyal customers should be the life blood of most companies, but often are underserved as it’s difficult to pull together and analyze all relevant data in a timely manner. This is a perfect fit for AI because there’s typically a lot more known data for AI to analyze about current customers, as compared to prospects. And it’s a project where you can start seeing high-impact results in weeks—perhaps even new revenue from customers who were previously inactive.
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For organizations, transforming finance and accounting function via adoption of topical technology means improving how they pre-empt red flags around the financial transactions within the organizations.
In fact, prudent finance and accounting operability represents the single biggest challenge firms have to deliver on their priorities, according to a survey by Econsultancy.
What’s more, 45% of respondents indicated that embedding analytics &AI relevant as possible in the finance and accounting function is their key focus.
CFOs around the world are not asking if digital disruption will occur, but instead, what it means for their function. So the question asked in this article, is how can CFO’s leverage digital transformation wave using AI to advance their organization’s competitive position and improve performance of their function ?
CFO of Tomorrow
With business around the world undergoing digital transformation, the roles of the c-suite is also changing. Perhaps most significantly, the role of the chief financial officer is moving from simply counting pennies to being a major driver of change within companies.
In the past, the role of the CFO was all about getting the numbers. That was 90 per cent of their time, but that is changing and now it’s about understanding the source of the number, understanding what created the number, and understanding the business drivers behind the number. A CFO then needs to try to make sense of the business drivers, and be able to present to the board what the outlook for the organization is, what the costs are, and what actions need to be taken.
This means that the CFO has to have a solid handle on data and analytics, and once they have that in their arsenal, they can become a strategic adviser to the business and they are able to tell the CEO things like what impact customer satisfaction has on the business.
I want to further highlight a few use cases showing how disruptive technologies such as artificial intelligence (AI) and machine learning will be used in the office of the CFO to increase productivity, simplify processes, and support decision-making, and aid in digital finance evolution:
Digital chatbots : Digital assistants for CFOs could impact analytics and the way they handle them. Today, almost everybody in Financial Planning & Analysis (FP&A) receives countless calls asking for information like, “What was our revenue in Q3 last year for this product? What has our growth been over the last three years for this line of business?”
Smart assistants like Amazon’s Alexa and Apple’s Siri can already answer questions on weather forecasts, stock quotes, and so forth, but what if they could provide the latest financial results and give decision makers instant access to information? A CFO could have a conversation with his or her ERP system using a digital assistant to get an immediate response or a clarifying question, without having to open a dashboard or dig into a database.
Risk assessments: When we assess commercial proposals for our services projects, we evaluate each project individually based on the customer characteristics – maturity, industry, size, current system landscape, and so on – as well as the complexity of the products to be implemented. To qualify this assessment, we depend on managers who have previously worked on similar projects. That can limit us to the individual perspective of those managers.
Machine learning could give finance teams and executives the power to access decades’ worth of projects, around the world, at the touch of a button. In levering these insights, teams could then develop a better-informed risk assessment, mapping the project against a much larger database of historical projects.
Invoice clearing: In finance departments today, accounts receivable or treasury clerks can often be challenged in clearing invoice payments, as customers often combine invoices in one payment, pay incorrect amounts, or forget to include invoice numbers with their payments. To clear the invoice, the employee then has two options: manually add up various invoices that could possibly match the payment amount, or reach out to the customer to clarify. In the case of short payment, the employee either has to ask for approvals to accept the short payment or request the remaining amount from the customer.
What if an intelligent system could help streamline this process by suggesting invoices in real time that might match the paid amount and, based on established thresholds, automatically clear the short payments or automatically generate a delta invoice?
Expense-claim auditing: Expense-claim auditing is another routine, transactional finance task. Finance teams are tasked with ensuring that receipts are genuine, match claimed amounts, and are in line with company policy. While state-of-the-art travel-and-expense solutions can simplify the process, a manual audit still needs to be performed.
Machine learning and AI technologies could improve this process, auditing 100% of all claims, and sending only questionable claims to a manager for approval. The machine could read receipts – regardless of language – to ensure that they are genuine, and match them against the policy.
Accruals: Artificial intelligence and machine learning also offer promise when it comes to determining bonus accruals. Today, teams have a myriad of factors to consider when determining bonus accruals. CFO teams look at current headcount salaries and bonus plans, and try to forecast all KPIs in compensation plans. From there, teams try to calculate the most accurate accrual (likely adding a buffer, to be safe). However, oftentimes, accuracy ends of being a matter of luck more than anything else.
By applying machine learning to these calculations, predictive analytics could serve as a valuable tool to generate unbiased accrual figures, leaving finance teams more time during closing periods for other activities that require human review and judgment.
Customer Journey: This is an area where the CFO is ideally placed to play a greater role in contributing to company growth and profits. His perspective on new customer acquisition, retention activities, customer development and predictive customer behavior models is crucial.
AI is what’s making all of this possible. With his new 360° vision and customer knowledge, the CFO can become a strategic business leader. Via AI and the breaking down of old company silos, the customer journey becomes everyone’s concern. And customer engagement wins its rightful place at the heart of business strategy.
The overall impact on jobs in finance: As these advanced technologies continue to penetrate the finance function, a new crop of skills are rising to the forefront when it comes to hiring finance talent. Routine, transactional roles will become less prevalent, while the need for strategic thinkers with cross-functional knowledge and technology prowess will be critical. Additionally, while transactional tasks will be fewer, digital transformation will require additional finance resources to be developed and supported, creating an opportunity to redefine processes and roles.
CFO’s, like everyone else, will have to adopt AI
In the future, it will be the companies that can harness AI that will set themselves apart. They will become fully digital businesses. Forward-thinking CFOs will help this happen. Because, by making AI accessible company-wide, they now have the power to unleash infinite company value.