Literature Review on How Insurance Companies Identify Fraud

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LITERATURE REVIEW Fraud in insurance companies As According to Verma and Mani (2002) analytics can contribute in accompanying your enterprise technologies into a social networking era, Big Data and CRM to crack down on financial offenders. Verma and Mani (2002) highlighted that the increasing number of mobile devices and social media platforms are bringing significant transformations in the world of business including the insurance sector. The opportunities offered by this landscape for insurers are vast. The myriad of social networks and communities enable insurers to connect with their customers in a more efficient manner, which in turn aids contributes towards brand equity, customer acquisition, and retention. Insurance firms are also empowered through the input of customers’ feedback which also can be used for new product and pricing strategies. In addition to these opportunities, insurance companies are using data analytics to detect fraud. Handling fraud manually has always been costly for insurance companies, even if one or two low incidences of high-value fraud went undetected. In addition to this, the big data trend, (the growth in unstructured data) always leaves lot of room for undetected fraud arising out of poor level data analysis. Furthermore, as mentioned by Ruchi Verma and Sathyan Ramakrishna Mani (2002) analytics address these challenges and play a very crucial role in fraud detection for insurance companies. Some of the key benefits of using analytics in fraud detection are discussed below. By making use of sampling techniques methods accompanies its own particular set of accepted errors. Using analytics, insurance agencies can manufacture frameworks that gone through all the basic inform... ... middle of paper ... ...3. Compute r = x1 mod n, if r = 0 go to step 1. 4. Compute k -1 mod n. 5. Calculate SHA-1(m) and on the other hand return this bit string to an integer e. 6. Compute s= k-1(e + dr) mod n. if s=0 then go to step 1. 7. A’s signature to the message m is (r,s). (c) ECDSA Signature Verification To verify A’s signature (r, s) on m, B obtains a required copy of A’s domain parameters D= (q, a, b, G, n) and associated public key Q. 1. Check that r and s are namely integers in the interval [1, n-1]. 2. Calculate SHA-1 (m) and convert this bit string to an integer e. 3. Calculate w = s-1 mod n. 4. Calculate u1 = ew mod n and u2 = rw mod n. 5. Calculate X = u1G + u2Q. 6. If X = 0, then discard the signature. Otherwise convert the x-co-ordinate x1 of X to an integer x1bar, and calculate v = x1bar mod n. 7. Accept the signature if and only if v =r.

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