JOB SEARCH

Date: June 22, 2010

Re: Manager, Fraud Modelling & Advanced Analytics

Location: Toronto, Ontario

Industry: Financial Services

Job Overview:

The mandate of this role is to perform requisite fraud analytics and build fraud predictive models to assure early detection of frauds pertaining to Insurance. The primary duties include the analysis of insurance claims and policy data to identify fraud risk drivers leading to effective predictive models, rules. exception reports, etc
 
Responsibilities:

• Reporting to the Head, Fraud Risk Analytics & Detection for the Enterprise.
• Build and maintain high quality fraud risk predictive models utilizing advance statistics, decision analytics, and machine learning to automate decisions and mitigate emerging loss scenarios under different types of fraud; back test such models regularly to assure efficacy and continuous improvement.
• Specifically, develop predictive models such as Logistic Regression, Decision Tree, Neural Network, Spatial, Bayesian Inference, etc. that contribute to early detection of frauds.
• Proactively analyze, predict and prevent fraud losses by devising innovative fraud detection strategies using relevant insurance data, including linear and non-linear, multidimensional transaction and non-monetary data, fraud trends, etc. to minimize fraud risk.
• Build and maintain a comprehensive enterprise-wide insurance fraud analytics program to proactively identify and mitigate emerging insurance frauds; provide insightful analyses and recommendations, including strategic direction for fraud containment, based on results from the fraud analytics program.
• Use advanced quantitative methods to assure data integrity, analyze complex relationships, unusual behavioral patterns, etc. and provide solutions that lead to effective fraud rules; perform customer profiling and segmentation analysis as required, leading to execution of specific mitigation rules, models and/or policy/process changes.
• Perform different advanced data mining techniques such as cluster analysis, CHIAD analysis, classification tree, etc. to acquire a comprehensive insight of fraud risk exposures that provides the necessary foundational strength for mitigation solutions.
• Research and analyse Insurance products, processes, methodologies, particular constraints, etc. the knowledge of which provide the bench strength for building effective fraud detection heuristics while assuring minimum impact on legitimate customers.
• Build and maintain a suite data extraction tools to derive best possible leverage for fraud detection from the various databases, including negative-file databases; document and maintain the library of such tools for use by other fraud management personnel.
• Build and maintain the requisite social network relationship tools to provide context and corroboration for fraud alerts from the models and/or rules analytics.
• Research and identify cutting edge data analytics tools/modelling techniques and justify the deployment of such within Fraud Management by presenting well articulated business cases.
• Liaise with other financial institutions and external parties to keep abreast of the newest technologies and strategies to manage fraud risks.
• Support the preparation of presentation material to highlight the effectiveness of fraud models, analytics tools, and newest technologies to assure senior management’s support for effective fraud management.

Required Competencies

• Strong analytical skills, including ability to analyze extensive linear/non-linear data sets (identify/analyse/deduce)
• Strong mathematical and statistical skills, including knowledge of neural, predictive models, decision trees, clustering, regression analyses, etc.
• Strong capability with data analytical/manipulation programs and concepts (e.g. SQL, SAS, Social Networks, etc.).
• Strong problem solving and critical thinking skills
• Strong interpersonal skills
• Strong verbal and written communication skills
• Strong PC skills including EXCEL, WORD, PowerPoint; familiarity with ACCESS an asset
• Sound technical knowledge IT concepts, including databases, datamarts/warehouses, and PC and Mainframe configurations
• Undergraduate degree in Statistics, Mathematics, Computer Science or related field; a Masters degree would be preferred.
• Minimum three years predictive, data and statistical modelling experience
• Strong SAS programming skills a must
• Prior fraud industry and insurance industry experience an asset
  

contact@tolstoy-resources.com

Website: www.tolstoy-resources.com