Seattle-based Zeitworks, which is launching its private beta today after raising a $4.5 million seed round in 2020, wants to give enterprises data-driven tools for improving the productivity of their teams and streamline their business operations. That's a market that's seeing quite a bit of growth right now, especially given how the pandemic has made remote work a standard business practice and how the overall talent crunch is forcing many businesses to do more with fewer employees. The overall idea here is to give businesses better insights into how teams work and where there are opportunities for improving business processes beyond simply using automation. "The problem that we're really addressing is that there's teams and companies in just about every industry who execute all kinds of repetitive business processes every day– and to be clear, it's business processes executed by humans," Zeitworks CEO and co-founder Jay Bartot told me. "Think about processing bank loans or insurance claims or HR onboarding of new employees, moving information from system to system. Oftentimes, those systems aren't interconnected or don't have APIs. The problem that we're solving is that the majority of these processes can't be optimized because they're undocumented and unmeasured. Unsurprisingly, understanding these processes is at the core of Zeitworks' product. But since these processes aren't documented, you can't exactly build a rule-based engine around discovering what people are doing. Instead, the company uses an AI-driven task mining system that uses signals from a wide variety of sources, mostly with a focus on the desktop applications these users interact with during their daily work. Bartot actually noted that he prefers the term'process intelligence' over'task mining,' given that task mining tends to be associated with creating RPA bots more than empowering teams and helping them work better. Now, in order to do all of this, Zeitgeist has to run its agent on an employee's desktop and those users' daily work is then tracked with quite a bit of granularity. Microsoft, with its Productivity Score, does something similar, but the company also faced quite a bit of backlash over it, given that managers could drill down to the individual employee and see how many emails they sent, chats they participated in, etc. The company later made some changes that put the focus more on the organizational level and away from individual users. "In our world, the kinds of productivity scores that we are recording are around this repetitive work -- the fact that people are processing bank loans or you know insurance claims repeatedly is a fundamental part of what we're measuring and what we're doing with pattern recognition," Bartot explained when I asked him about the potential for backlash. "So the productivity scores are really geared towards that specific kind of repetitive work.

Nowadays, there are numerous risks related to bank loans both for the banks and the borrowers getting the loans. The risk analysis about bank loans needs understanding about the risk and the risk level. Banks need to analyze their customers for loan eligibility so that they can specifically target those customers. Banks wanted to automate the loan eligibility process (real time) based on customer details such as Gender, Marital Status, Age, Occupation, Income, debts, and others provided in their online application form. As the number of transactions in banking sector is rapidly growing and huge data volumes are available, the customers' behavior can be easily analyzed and the risks around loan can be reduced.

So, it is very important to predict the loan type and loan amount based on the banks' data. In this blog post, we will discuss about how Naive Bayes Classification model using R can be used to predict the loans. As there are more than two independent variables in customer data, it is difficult to plot chart as two dimensions are needed to better visualize how Machine Learning models work. In this blog post, Naive Bayes Classification Model with R is used.

The financial services industry, especially the banking sector, is going through considerable change. This is due to the implementation of digital technology and as a consequence of changing customer behavior. These behaviors include different expectations in terms of service and a desire to access baking services through multiple channels. For banks, the motivations for implementing digital technologies are driven by changing customer perceptions and a desire to lower cost and to mitigate risks increased security. With changing consumer trends, a report by i-Scoop found that around 90 percent of consumers prefer on-line banking as opposed to going to the bank.

Nowadays, there are numerous risks related to bank loans both for the banks and the borrowers getting the loans. The risk analysis about bank loans needs understanding about the risk and the risk level. Banks need to analyze their customers for loan eligibility so that they can specifically target those customers. Banks wanted to automate the loan eligibility process (real time) based on customer details such as Gender, Marital Status, Age, Occupation, Income, debts, and others provided in their online application form. As the number of transactions in banking sector is rapidly growing and huge data volumes are available, the customers' behavior can be easily analyzed and the risks around loan can be reduced.

While it's difficult to predict what path the Artificial Intelligence industry will take one thing is for sure - the world's biggest tech giants are investing heavily in this. AI is also taking the banking industry by storm with fin-tech. One of Canada's largest banks, CIBC, just announced a deal to team with AI-enabled lender Borrowell to allow consumers to apply for small loans. Globalive Capital Chairman Anthony Lacavera tracked all of the latest trends at one of the top AI conferences in Toronto titled CDL Machine Learning. He says banks could soon use machine algorithms to measure, for example, which clients could default and when.

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These changes are called "Industry 4.0" or the fourth industrial revolution. It is an industrial revolution that uses artificial intelligence and robots in such a way that manufacturing plants will become unmanned and a majority of office jobs will be made unnecessary. In March, an AI player of the board game go, developed by Google and named AlphaGo, defeated the world's leading professional go player 4 games to 1. The pro lost the first three games, and although he won the fourth, he was defeated in the fifth round. The decisive factor that led to the victory for AlphaGo was its "deep learning" capability.