In many of these developing nations less than 20–30% of the citizenry have bank accounts. The other 70% of the population is without access to a bank or financial services and have been deemed “the unbanked”.
This article is the second of a two-part series on Robotic Automation for customer engagement. Part one provides an overview of the technology. It explains how it can be used to assist customer service representatives and automate tasks of customer-facing processes. Part two explores the art of the possible and dig into the three most common models for leveraging this style of automation. But first, we need to do away with two misconceptions.
Artificial intelligence (AI) is the ability of machines or computers to emulate human thinking or decision-making. After years of speculation into the technology and its possibilities, AI is starting to deliver on its promise. Here are eight stats that prove natural language processing, machine learning and cognitive computing are helping businesses to deliver excellent customer service.
Data governance means understanding the data assets of the organization – knowing that they are of good quality, accessible and secure and that using them does not put the company at legal, reputational or financial risk while also enabling it to be agile. Now, the age of Data Governance 2.0 is dawning. It’s an age that must be marked by everyone within the business collaborating in data governance.
The new generation of fintech tools offers the potential to help consumers manage their increasingly complicated financial lives, but also poses risks that will need to be managed as the marketplace matures.
Global financial companies enhance digital services to attract new customers across the whole financial ecosystem. Fintechs and software giants successfully exploit their corresponding niches. In turn, the vast majority of community banks have no distinct advantages over their competitors.
With the evolution of the digital landscape, tapping into text, or Natural Language Processing (NLP), is a growing field in artificial intelligence and machine learning. This article covers the common pre-processing concepts applied to NLP problems.
Procurement has unfortunately often been a laggard in adopting new technologies. However, with the proliferation of AI across so many aspects of an organization, there is no reason for it to sit on the fence on this one. The gains can be monumental, both in terms of bottom-line impact as well as the overall effectiveness of how the procurement function contributes to an organization’s larger goals.
While economic self-sufficiency powered by financial technology makes the PAYG sector attractive for commercial investment, it is also one of the most socially impactful enterprise models in existence today. By prolonging working and study hours, reducing health hazards, reducing CO2 emissions and other environmental damage, the PAYG industry will have a strong and measurable impact on the well-being of its customers and the planet.
Artificial intelligence can predict stocks, diagnose patients, hire job applicants, play the games of chess and go, and do many more tasks on par or better than humans. Humans still have an advantage, however: They have intelligence at the edge.
The reality is that the conversation about AI is increasingly important, so important, that Max Tegmark, a physicist at MIT and author of Life 3.0: Being Human in the Age of Artificial Intelligence, claims the conversation to be the most important conversation of our time.
The cyber security industry is a good example of a field where artificial intelligence (AI) is both being looked to as a near-magical perfect solution while also already being deployed in a practical way every day. But can we trust it?
Given the difficulty in running a successful data science project, there are some specific reasons why these kinds of projects fail. Data science is a field that requires an interdisciplinary skillset. You need to be good at math and statistics, which yields a foundation of methods to analyze and interpret data. Domain knowledge is required to understand the data and the (business) processes that shall benefit from the analysis. Coding is a prerequisite to bring the theory to action.
Big data is gradually percolating every technology from AI to autonomous cars to IoT, with companies continuing to invest heavily on Big Data Analytics. However, if you blindly spend on building infrastructure and buying tools, without really thinking about how to derive value from your data, then it will only do more harm than good for your organization. Here are 5 ways to ensure that your big data investment achieve the desired result and deliver the promised value for your organization.
How do you ensure that your first IoT project implementation will be successful? In this post, I’ll share ten best practices for managers planning their first IoT project.
Robotic Process Automation (RPA) initiative is akin to a 'Gold Rush', where the RPA-led savings will be high for a few and selected processes (read, harmonised) and the subsequent effort for automation will be significantly higher, beyond the catchment area. Robotic Process Automation is the automation of repeatable tasks by using artificial intelligence, thus reducing the need for human effort.
Some countries’ governments have clamped down on virtual currencies. Some embrace them as a fast-forward to a more stable national currency. Meanwhile, in the private sector, JPMorganChase CEO Jamie Dimon earned a place in the Bitcoin hall of fame vowing to fire any employee who traded in the currency, while Goldman Sachs is exploring trading in Bitcoin at some point.
Hadoop has the advantage over relational database management systems, and its value will continue to increase for businesses of all sizes as our world's caches of unstructured data continue to increase all around the world. For this reason, leveraging Hadoop's big data services is of growing importance to more organizations than ever before.
There’s not only a high demand for data scientists but also those who know how to visualize and present data in an effective and persuasive manner.This is where data storytelling comes into the picture.A combination of data, visuals and narrative, data storytelling is the hot, new data science skill everyone will need in the future.
Today’s IoT technologies are still immature point solutions that address emerging use cases with evolving technology standards. Buyers are concerned that what they buy today may become functionally or technologically obsolete tomorrow. Faced with this dilemma, many defer buying even if the IoT solutions they buy today offer tremendous value to their organizations. This post discusses a planning strategy called “future-proofing” that helps managers, buyers, and planners deal with obsolescence.
Look, you could say AI is already teaching us. You ask questions to search engines all day, and get answers in return. If you train them right, algorithms can feed you valuable knowledge. However, you cannot ask it WHY it gives you those answers and articles. Good teaching is an open two way process.
Tooling is probably the least exciting topic in data science at the moment. People seem to be more interested in speaking about the latest chatbot technology or deep learning framework. This just does not make sense. Why would you not dedicate enough time to pick your tools carefully?
Suddenly, everyone has an “innovative” IoT platform, “smart” connected devices, machine learning and “disruptive” pricing models. But don’t be fooled by the hype. While IoT may be built with innovative technologies, the real IoT innovation is what they allow organizations to become — intelligent, agile, and adaptive.
Robotic process automation (RPA) seems to promise the Holy Grail to operations. Lower cost, fewer errors, better compliance with procedures – the benefits seem real and achievable to COOs and operations leaders. The fact that RPA tools promise to pay for themselves from the operational savings (with short payback periods) makes the business case even more attractive.