Risk & Fraud Analytics
Market Risk Analytics, Credit Risk Analytics, Insurance & Banking Fraud Analytics and more...
Risk & Fraud Analytics
  • Insurance Fraud Analytics

    Risk management has always been an integral part of the insurance business. Firms succeed through their ability to identify and manage risks facing their clients. Successful insurers not only understand risk clearly, but also dynamically use available insights to handle exposure to risk through measures aimed at avoiding or preventing losses and screening, pre-empting and pricing them-in in the underwriting process.

    Fraudulent claims are a serious financial burden and impact the financial health not only of insurers, but also of innocent people seeking effective insurance coverage. Predictive analytics can help in insurance fraud detection by uncovering the likeliness of frauds arising in medical billing, life insurance, claims and other areas of the business.

    Data scientists today can leverage complex and sophisticated capabilities such as predictive modeling, text mining, database searches, anomaly detection and network link analysis to create an advanced and powerful fraud analytics?engine.

    Our experts can help check claims fraud by:
    1. Identifying duplicate claims.
    2. Flagging up suspicious transactions for a detailed follow-up.
    3. Decrease insurance fraud losses by detecting and preventing fraud before claims are paid leveraging an advanced fraud modeling engine.
    4. Persistently improving historical models to address changes in insur?ance fraud trends.
    5. Determining dealers/agents with a high numbers of claim payouts.
    6. Attaining fewer false positives that translate into greater customer satisfaction.
Risk & Fraud Analytics Expert
Related Work
Fixed Price - Est. Time: 3 months,

·Conduct analysis to address critical business challenges faced by client teams working in different markets across the banking value chain to recommend the best data and insights powered solutions

· Gathering business requirements to create high level business solution framework covering aspects of banking customer journey, policy & regulatory requirements, data driven business process design & Technology Architecture

·Interface with client teams along with inputs from internal research and data insights teams to develop solutions for identified opportunities across banking LOBs

·Build analytical use cases in AI and Machine Learning across different banking functions including Operations, Treasury and Finance, Risk assessment, Compliance and Regulatory, Fraud, IT and Technology, Product design and Marketing, AI driven Customer Experience

· Ability to work with large data sets and present findings / insights to key stakeholders; Data management using databases like SQL, Oracle, S3 buckets and likes of the same

· You should have worked for 4-6+ years in the area of Knowledge Strategy and Consulting, Analytics and AI Delivery and Business Operations in any of the Tier-1 consulting organizations (McKinsey, BCG etc.) with focus and experience in Banking industry

·Experience in at least some banking function like Operations, Treasury and Finance, Risk assessment, Compliance and Regulatory, Fraud, IT and Technology, Product design and Marketing, AI driven Customer Experience

·Experience in at least some areas of banking domain like Cards/Payments/Retail or Commercial Banking/Digital/Mortgages or Regulatory/Data Privacy/Asset Management/CDD/KYC/AML or Contact Center/Branch Banking along with knowledge of various banking products is required

·Have experience in building analytics models in atleast 1 or more banking function for cross-sell up-sell models, churn models, forecasting models, fraud detection models, AML models, default prediction models, risk pricing models etc.

·Hands on experience in building and deployment of Statistical Models/Machine Learning using following techniques – Statistical Algorithms (at least some of them)
a. Segmentation and Predictive Modeling (K means, Clustering, Multivariate Regression, Logistic Regression)
b. ML algorithms –RF, GBM / XgBoost, SVM
c. NLP algos – TFIDF, Word2vec, LDA
d. Exploratory Analytics (T-tests)
e. Decision Trees

·Experience with some databases preferred like Oracle, TD, Sybase, MS SQL, Win SQL, S3 buckets

·Experience and exposure to developing cloud solutions on AWS/GCP/Azure

·Good experience with Data, Analytics and AI technologies & tools – data native mindset with a deep understanding of Statistics and experience in A/B or multivariate experiment set-up, sharing testing results & recommendations

·Ability of Visual story-telling with BI & Visualization Tools (Tableau/Spotfire/Power BI, Cognos, Business Objects) is preferred

·Advanced Excel including VBA and PowerPoint skills

·Consulting skills Experience preferred

·Excellent written and oral communication skills with ability to clearly communicate ideas and results to non-technical business people

·You are laser focused on delivering outcomes with speed and agility

·You are self-driven and thrive in ambiguity

·MA/MBA/MSc/MPhil.  in Statistics / Economics/ Mathematics / Engg. Degree from Top Colleges in India or Abroad

 Experience:

  • 4 to 6 years

Job location:

  • Bangalore

Duration:

  • 3 months. May get extended.

Joining:

  • immediate