Analyst - Risk Management II
More jobs by this advertiser
Why American Express?
There’s a difference between having a job and making a difference.American Express has been making a difference in people’s lives for over 160 years,
backing them in moments big and small, granting access, tools, and resources to take on their biggest challenges and reap the greatest rewards.
We’ve also made a difference in the lives of our people, providing a culture of learning and collaboration, and helping them with what they need to succeed and thrive. We have their backs as they grow their skills, conquer new challenges, or even take time to spend with their family or community. And when they’re ready to take on a new career path, we’re right there with them, giving them the guidance and momentum into the best future they envision.
Because we believe that the best way to back our customers is to back our people.
The powerful backing of American Express.
Don’t make a difference without it.
Don’t live life without it.
Function Description:The CFR team helps drive profitable business growth by reducing the risk of fraud and maintaining our customers' confidence in the security of our products. It utilizes an array of tools and ever-evolving technology to detect and combat fraud, minimize the disruption of good spending and provide a world-class customer experience. The team leads efforts that leverage data and digital advancements to improve service and risk management as well as enable commerce and drive innovation. CFR is responsible for developing and monitoring statistical models for predicting individual and commercial' behaviors such as credit risk, fraud risk, spending and revolve. These models are used for key business decisions made across the customer life cycle to manage risk and accelerate profitable business growth. Underpinning our growth as a company are the tools and capabilities that ensure we prudently take and manage risk in a viable way.
Purpose of the Role:
Develop and enhance existing American Express statistical fraud models by leveraging best-in-class modeling techniques and data across various stages of card member lifecycle.
1. Develop and enhance statistical models by utilizing the best-in-class modeling techniques with available data.
2. Track and monitor the performance of fraud models and conduct reviews.
3. Evaluate new data sources/new variables for their efficacy in discriminating between fraudulent and genuine transactions based on statistical techniques and business judgment.
4. Partner with other teams to develop system capabilities, models, controls and designing data elements.
Critical Factors to Success:
• Drive billing, revenue growth and profitability through advanced analytical techniques
• Ensure Modeling Accuracy and enhance modeling efficiency in existing processes using Machine Learning
• Innovate Modeling Techniques and Variable creation
• Put enterprise thinking first, connect the role’s agenda to enterprise priorities and balance the needs of customers, partners, colleagues & shareholders.
• Lead with an external perspective, challenge status quo and bring continuous innovation to our existing offerings
• Demonstrate learning agility, make decisions quickly and with the highest level of integrity
• Lead with a digital mindset and deliver the world’s best customer experiences every day Qualifications
0-6 years with relevant experience in Analytical/Modelling Skills
Preferred: Experience in R/Python programming and/or Statistical modeling
Post Graduate in Statistics/Mathematics/Economics/ Engineering/Management
Data Science/Machine Learning/Artificial Intelligence
o Expertise in Coding, Algorithm, High Performance Computing
o Unsupervised and supervised techniques - : active learning, transfer learning, neural models, Decision trees, reinforcement learning, graphical models, Gaussian processes, Bayesian models, Map Reduce techniques, attribute engineering
o Deep learning
o Gradient boosting machines, self-reinforcing algorithms
Analytics & Insights & Targeting o R, Python, C, C++, Java, SAS SQL
o Advanced Statistical Techniques
o Data correlation
o Model Accuracy Techniques : Gini, Concordance, F-Score
Knowledge of Platforms:
Big Data – Cornerstone
Enterprise Leadership Behaviors
• Set The Agenda: Define What Winning Looks Like, Put Enterprise Thinking First, Lead with an External Perspective
• Bring Others With You: Build the Best Team, Seek & Provide Coaching Feedback, Make Collaboration Essential
• Do It The Right Way: Communicate Frequently, Candidly & Clearly, Make Decisions Quickly & Effectively, Live the Blue Box Values, Great Leadership Demands Courage