Companies are increasingly discovering the beneficial link between machine learning and risk assessment. Machine learning can analyze variables faster than humans, helping businesses identify threats and address them. Successful applications exist in industries ranging from finance to health care.
Risk management occurs when businesses forecast the things that could adversely affect their finances and assess how to minimize those threats. When companies excel at risk management, they’re better able to plan for what could happen and determine how to respond if those situations occur. A growing body of evidence suggests machine learning and risk management is a smart combination.
Machine learning is a subset of artificial intelligence (AI) that can spot patterns in data to make conclusions from them. Moreover, it can get smarter over time as the amount of data it processes goes up.
The Rise of Machine Learning for Loan Approvals
When customers apply for loans, they typically have to submit documents that verify their employment and income details. Banks will also check their credit scores. The waiting period between the time when people send their information and wait for responses can be agonizing.
Still, lenders understand how crucial it is not to rush their decisions. They want to verify applicants will feasibly be able to pay back their loans, and, therefore, reduce the risk to the company. Machine learning can speed up the approval process without increasing the company’s risk.
After comparing an applicant’s statistics to data points from thousands of other past customers, it generates a risk score that determines whether to grant a person’s loan request or deny it. A primary advantage of this approach is that machine learning makes competent decisions faster than humans could. Applicants don’t experience anxiety over such prolonged periods, and companies avoid haphazard choices.
The Worthiness of Using Machine Learning to Increase Safety
Numerous recent events collectively caused school officials to take risks associated with school violence especially seriously. In addition to the threat to life, they must navigate reputation-based concerns. For example, if parents perceive a private school is not adequately minimizing the risk of a shooting, they will enroll their kids elsewhere. Then, the educational institutions deal with reduced profit potential.
A research team at Cincinnati Children’s Hospital Medical Center carried out a pilot study regarding machine learning and risk management for predicting school violence. It found machine learning was as accurate as a team of psychiatric experts for determining individuals’ risk for perpetrating such acts. The machine learning tool achieved an accuracy rate of more than 91%Opens a new window by using data collected during student interviews.
Technology exists that can identify an active shooter in a crowd, too. The trained algorithms’ object-recognition capabilities tell the difference between someone carrying a gun or a hammer. Then, if the system positively identifies an active shooter, it could automatically lock doors in a building and notify law enforcement personnel.
It’s easy to understand how this technology could help people feel more confident about the safety precautions in place at an event. For example, by holding an indoor festival at a venue with that kind of machine learningOpens a new window technology, a concert promoter could make artists feel more confident about performing there and agreeing to be part of the gig.
One of the best ways to deal with risks for any project is to adopt a risk mindsetOpens a new window . A person or organization’s risk tolerance changes with experience. Every member of a team should take risk management seriously and know they have valuable ideas to bring up. Machine learning is particularly effective for assessing risks — associated with safety or otherwise — because of the sheer amount of data it can process in such short periods.
The Applications of Machine Learning to Improve Personalized Care
Certain events or characteristics can make any hospital’s emergency room busier than normal on a given shift.
For example, if a town is hosting an event where people often overindulge on alcohol, staff members might learn to expect an above-average number of people admitted due to alcohol-related complications. Or, holidays that people traditionally spend visiting family might increase the number of patients involved in car crashes. The likelihood goes up due to things like the increased number of vehicles on the road and the chances of people driving for too long without resting.
Since most hospitals operate as businesses and simultaneously provide the best treatment for patients, they have to crunch numbers and ensure they function economically. That means, in part, figuring out how to reduce readmission rates. If hospitals consistently don’t provide patients with the care they need to stay healthy after getting discharged, the facilities’ expenses and labor needs will rise.
Researchers created a machine learning model that evaluated 382 variablesOpens a new window to determine a patient’s readmission rate. The factors ranged from a person’s lab results to their marital status. The team believes their accomplishment could aid hospitals in making better decisions while allocating the time and money spent. It’s then easier to get an admitted patient ready to go home without soon returning to the hospital.
This application of the technology benefits both hospitals and patients. Hospitals can use the machine learning assessment to ensure patients get the most appropriate care that lets them avoid complications. Plus, people who need hospital care can limit incidents that warrant stressful return trips and potential exposure to antibiotic-resistant infections.
Compelling Ways to Manage Risks
One consistent reality about risks is their inevitability. However, being aware of the risks and taking steps to reduce them helps businesses remain profitable and respected in their particular industries.