A predictive maintenance model uses sensors and IoT technology to monitor the condition of equipment and predict when it will need repairs or replacements. This equipment may be anything from personal cars to large factory machines.
The machine learning programs used to run these models tend to take either the regression approach or the classification approach. Regression produces more accurate results but relies on more data; classification best predicts equipment failure within a set time period.
The abundance of job seekers nowadays leads many companies to use machine learning throughout their recruitment process. Some examples of this can be found in the fields of talent management, talent attraction, and candidate selection.
Talent management entails understanding the best fields, positions, and backgrounds to set talent up for success. Talent attraction selects the top outlets and channels for attracting the most suitable employees. Finally, candidate selection use neuro-linguistic programming (NLP) tools on CV's to find suitable candidates.
Banks, credit unions, credit card companies, insurance companies, stock brokerages, investment funds, and more must report their activities to government regulatory agencies. Following financial crises in the late 2000s, regulatory compliance has become stricter and more onerous on financial services companies like those listed above.
From the stricter need for reporting and the massive amounts of data generated by financial institutions, the regtech industry has sprung up, combining regulatory reporting and big data technology.