Systems and methods for property damage prevention and mitigation

A computer-implemented method for training computer models associated with functionality of property devices, the method implemented by a computing system including at least one processor in communication with a memory device and one or more telematics sensors associated with one or more property devices of a property, the method comprising:

• training, using machine learning tools or artificial intelligence, one or more computer models by inputting a plurality of property parameters into the one or more computer models;
• receiving, via the one or more telematics sensors, property telematics data associated with a functioning of the one or more property devices;
• re-training, using the machine learning tools or artificial intelligence, the one or more trained computer models by inputting the property telematics data into the one or more computer models;
• outputting, from the one or more re-trained models, a likelihood of damages to the property due to the functioning of the one or more property devices;
• determining, using the one or more re-trained computer models, one or more damage factors including at least one aspect of the property that increases the likelihood of the property incurring the damages;
• and
• outputting, from the one or more re-trained computer models, one or more recommended actions associated with the functioning of the one or more property devices for mitigating the likelihood of the property incurring the damages.

At least one non-transitory computer-readable storage medium having computer-executable instructions embodied thereon, wherein when executed by at least one processor communicatively coupled to a memory, the computer-executable instructions cause the at least one processor to:

• train, using machine learning tools or artificial intelligence, one or more computer models by inputting a plurality of property parameters into the one or more computer models;
• receive, via the one or more telematics sensors, property telematics data associated with a functioning of the one or more property devices;
• re-train, using the machine learning tools or artificial intelligence, the one or more trained computer models by inputting the property telematics data into the one or more computer models;
• output, from the one or more re-trained models, a likelihood of damages to the property due to the functioning of the one or more property devices;
• determine, using the one or more re-trained computer models, one or more damage factors including at least one aspect of the property that increases the likelihood of the property incurring the damages;
• and
• output, from the one or more re-trained computer models, one or more recommended actions associated with the functioning of the one or more property devices for mitigating the likelihood of the property incurring the damages.