Mathfi-Risk © 
Classifier/Predictive AI

                Log in/Register                Request a Demo                Instruction Manual

A new AI-powered service helping businesses and their risk or decision experts to make binary and multi-class risk decisions by performing binary or multi-class predictions and classifications for example or sanity checking their existing risk assessments. Some examples:

1. Helping Auditors Assess the Risk of Businesses: Knowing the existing businesses’ data features such as past records of audit office, audit-paras, environmental conditions reports, firm reputation summary, on-going issues report, profit-value records, loss-value records, follow-up reports, multiple risk factors, the segment business belongs to (Public Health, Buildings and Roads, Forest, Corporate, Animal Husbandry, Communication, Electrical, Land, Science and Technology, Tourism, Fisheries, Industries, Agriculture and more) etc, and having correctly established whether those existing businesses have been high risk fraudulent business or not, decide or predict whether a new business (under audit and risk assessment) is also high risk fraudulent business and with what probability?

2. Risk Modelling, The Loan Default Risk: Knowing the past customers’ data features such as age, gender, education, monthly income, credit card balances, past loan type (cash or revolving), marriage status, car model and year, external credit scores, number of dependants, loan annuity, consumer loans including the price of the goods for which the loan is given, income type (businessman, working, maternity leave, etc.), family status (married, single, divorced, etc.), partner monthly income, average income in client’s neighbourhood, the housing situation of the client (owning, renting, living with parents, etc.), house value, number of days before which the customer started current job, number of days before which the customer changed the identity document with which he applied for the loan, week day and time of application, customer’s job sector, the cash requested for during past loan application, term of previous credit, relative to date of current application the date of the first disbursement of the previous application, the first due supposed to be of the previous application, the expected termination of the previous application, the instalments as of when it was supposed to be paid and when it was actually paid (all in days), insurance requested (yes or no), and any other relevant data features and having verified and labelled whether those past customers/applicants have defaulted on a payment within a period or not (target and label of the prediction), predict whether a new customer will default on a loan payment at a certain point of time in future or not (and with what probability, the risk metric)?

3. Help insurance underwriters to estimate the loss/risk levels of commercial buildings: Butterfly AI can help underwriters to classify the commercial buildings based on their potential loss and risk level. Knowing the key data features of hundreds of past insured  commercial buildings including building age, number of floors, square footage for each floor level, the overall score of building in terms of being fire resistant (for example fire-resistive by spraying fire-retardant chemicals and using building materials such as concrete or brick and fire-proof roofing materials), number of past insurance claims, category of past claims (e.g. fire, flood, wind damage, etc.), quality grade of water pipework (1,2,3), quality grade of gas-work (1,2,3), quality grade of electric wiring (1,2,3), quality grade of electric outlets (1,2,3), quality grade of heating system (1,2,3), quality grade of cooking equipment and kitchen facilities (1,2,3), the lightening protection system grade (1,2,3), quality grade of construction material used for exteriors (1,2,3), type of the construction material used on exteriors, e.g. fire resistance (1,2,3), quality grade of material used in flooring (1,2,3), quality grade of material used in ceiling (1,2,3), quality grade of material used on walls (1,2,3), earthquake resistance grade (1,2,3), quality grade of material used in wall insulators (1,2,3), the quality level of maintenance practices (1,2,3), the quality level of repair practices (1,2,3), history of bushfire across the surrounding vegetation, distance from epic centre of past earthquakes,  on the route of past tornados, distance from epic centre of past hurricanes, distance from the frequently flooding rivers, possibility for damaging winds, local wildfire risk,  history of the external floods,  history of internal flooding, the distance from firefighter brigade station, numerical ranking of combustibility of contents, which measures their effect on the structure under fire conditions, numerical ranking of susceptibility of contents, which measures potential damage to materials or merchandise from the effects of fire, smoke, and water, the quality grade of the sprinkler and extinguisher protection credits, the nature and number of claims within the surrounding buildings if they are exactly same as the target building, presence of vegetation or dead vegetation within ten feet of the commercial building, presence of the any combustible materials within rain gutters, double-pane windows, building’s security grade, hazardous material stored in the building, hazardous material stored in the neighbouring buildings, data on adjacent buildings, including exposed walls, hazards, construction, and distance (for example, a property near a high-hazard operation or next to a storage tank with flammable liquids), the number and category of claims of the buildings in that district which are the closest to the target building in terms of building profile (the most similar buildings to the target building in terms of data features described above including material, grades, and number of floors, etc.), and having verified, recorded and labelled the past loss/risk level of those commercial buildings ( as 1 (lowest), 2, 3, 4, 5 (highest), the targets and labels of the classification/estimation), classify a new candidate commercial building based on the loss level (and present the probability and certainty of classification), provided that the data features (described above) are available for that new candidate building.

and any other binary or multi-class risk-related classification assessment or prediction use cases.

                Log in/Register                Request a Demo                Instruction Manual