Abstract
The high rate of fraud in the diversion of cargo and vehicles, as well as the large number of accidents on Brazilian highways, have made insurance negotiation less and less advantageous for insurers, often making this type of business unfeasible, common to several types of goods. On the other hand, risk management companies that work with complex operations and large amounts of data have shown a recurrence of operational failures caused by the overload of manual work required from operators who monitor vehicles and loads, thus generating losses that even destabilize this model business Most of the existing computational solutions in the insurance market are focused on other logistical aspects dispersed in different solutions, and, to date, none of them have specific functionalities for the management and risk mitigation of insured cargo. In view of this scenario, Shamah Seguros de Transportes, a company created in 2009, which operates in the National and International Transport Insurance market, with more than 300 policyholders, conceived a proposal to increase its competitiveness in the logistics and insurance market, in addition to participating as a partner of other insurers and transportation companies in the management and mitigation of risks in the flow of transport of insured cargo, which ranges from the collection process to the completion of cargo delivery. Thus, it is proposed as a project objective to build the MVP of the Cloud platform for automation of management in land transport logistics operations with insurance coverage, through the treatment of shipment data for the purposes of registration and elimination of evasion. insurance, called 'Smart Load'. Applied research consists of three blocks: a) deepening of field research with managers and logistics management professionals; b) Modeling and mapping business rules and processes; and c) Development of a functional MVP prototype using agile methodologies. This solution is justified, as it will provide, in a holistic way, improvement of operational performance, obtaining greater security, transparency and credibility to the operations between the actors in this ecosystem. In addition, the solution to be developed will also provide for reductions in time, costs, claims and fraud risks in the diversion of cargo, thus preserving the financial health of the various actors involved, formed by carriers, shippers, whether importers or exporters, insurance brokers cargo insurance specialists, insurers, risk managers, monitoring technologies, drivers and recipients. Some features tend to be improved, such as the risk analysis of cargo transportation, which will be supported through the use of Machine Learning techniques, which may indicate the least risky route for the professional, the required driving time, in addition to complying with all risk management rules such as the use of trackers, the use of electronic lures, the use of armed escorts, best stopping points, among many other requirements included in insurance policies. In addition to these improvements, it will be possible, through services already carried out, to generate a safe and practical database, allowing the creation of a positive and relevant community of professionals working in this area. (AU)
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