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Fraud Analyst III

extra holidays

Remote:

Full Remote

Contract:

Full time

Salary:

4 - 4K yearly

Experience:

Mid-level (2-5 years)

Work from:

Mexico

Offer summary

Qualifications:

Bachelor's degree in Engineering, Mathematics, Actuary, Economics or similar., Over 3 years of experience in analytical areas., Proficiency in database management via Python, SAS, SQL, and Tableau., At least 2 years in consumer credit risk analysis..

Key responsabilities:

Construct strategies for fraud prevention.
Coordinate investigations for fraud patterns.
Prepare executive reports and presentations.
Analyze fraud trends at portfolio and merchant levels.
Mentor and support junior analysts.

Kueski Fintech: Finance + Technology SME https://kueski.com/

501 - 1000 Employees

See more Kueski offers

Job description

About Kueski

At Kueski, we're dedicated to improving the financial lives of people in Mexico. Since 2012, we've been the leading buy now, pay later (BNPL) and online consumer credit platform in Latin America, known for our innovative financial services. Our flagship product, Kueski Pay, provides seamless payment solutions for both online and in-store transactions, establishing itself as the preferred option for a quarter of Mexico's top e-commerce merchants. Notably, we were the first to introduce BNPL on Amazon Mexico.

We're a tech company with a culture geared toward innovation, collaboration, and impact, fostering a strong, diverse, and inclusive company culture. In 2023, Kueski was recognized as the top BNPL platform by Fintech Breakthrough and earned the title of one of Mexico's most ethical companies from AMITAI. Additionally, we ranked as one of the Best Companies for Female Talent by EFY.

Purpose

Analysis of the behavior of the portfolio to define credit approval and fraud prevention strategies for the different credit products, such that business growth expectations are achieved, taking care of the level of losses, profitability, and optimal operating cost. Collaborate with multifunctional teams in the definition and implementation of analytical projects, infrastructure, and various control measures aligned with the established risk appetite.

Key Responsibilities

Construction of strategies for sustainable business growth and fraud prevention, through the development of monitoring systems and the definition of credit evaluation schemes, through an analytical and numerical perspective

Coordination of investigations for the massive identification of fraud patterns, derived from alerts, experiments, and control groups, through massive data analysis

Preparation of executive reports and presentations at a strategic level

Analysis of fraud in two levels: at portfolio to identify trends and suspicious behavior in customers (B2C) and at merchant/branch level identifying trends and patterns that may suggest fraudulent actions by store/merchant representatives (B2B). By developing methodologies for projecting credit losses associated with fraud, using data analysis methodologies, decision trees, clustering and other models.

Quantitatively and qualitatively evaluate the need to incorporate additional information, new tools and/or new preventive policies to mitigate losses and prevent fraud

Coordinate and develop proofs of concept from external data sources to evaluate added value for fraud prevention and loss mitigation

A key point of contact with engineering and product to ensure appropriate development and implementation of fraud and risk policies and tools

Analysis of fraud and credit risk profiles to establish cross-selling strategies and campaigns to pre-approve credits in populations with good behavior to maximize the permanence and roots of clients with Kueski

Stay up to date with industry best practices, regulatory guidelines and market developments to ensure compliance and promote continuous improvement in fraud risk management processes.

Mentor and support junior analysts, providing guidance and experience in fraud risk analysis methodologies and techniques

Position Requirements

Graduate from one of the following majors: Engineering, Mathematics, Actuary, Economics or similar

More than 3 years of experience in analytical areas with proven experience in database management via Python, SAS, SQL and Tableau

At least 2 years of experience in analysis related to consumer credit risk behavior and financial fraud prevention

Knowledge of analytical techniques for fraud prevention, consumer credit risk analysis, segmentation techniques and project management

Strong analytical skills, and knowledge of statistical and financial tools

Analysis of information available in databases via SQL queries, Athena, Python, Tableau/Databricks and Excel

Knowledge of fraud prevention and credit risk issues with consumer credit products

Advanced English

Operation of Data Sources and experience interpreting analytical tools associated with fraud prevention

Understanding fraud and consumer credit risk metrics

You’ll love working at Kueski because

We have a mission-driven culture focused on customer value, teamwork, humility, and integrity

Everyone is expected to have role clarity, career growth, and a personal development plan. Feedback and recognition are embedded in our company processes, systems, and practices

We ensure competitive salary, medical insurance, and wellbeing through ample and flexible time off as well as mental healthcare benefits

Everyone is an owner and eligible for competitive stock options with a company poised for success. We're committed to building an inclusive and diverse team and we know this leads to incredible work

Kueski: Where talent excellence improves Mexican lives.

#LifeAtKueski #KueskiTalent

At Kueski we embrace diversity in all forms, systematically promote equity, and ensure everyone feels included with a sense of belonging. We are committed to the full inclusion of all qualified candidates. As part of this commitment, we will make efforts to ensure reasonable accommodations are made during the hiring process. If reasonable accommodation is needed, please let the Talent Acquisition team know.