BankservAfrica form part of the South African National Payments System and are a trusted partner of the financial industry, including banking institutions, and therefore require that employees adhere to unwavering standards of honesty and transparency in performing their duties.
PURPOSE
The Fraud Business Intelligence Analyst has a dynamic role to play at a national level in the analysis, interpretation, and visualisation of payments data. Experienced in data modelling, analysing fraudulent patterns and behaviour, you will through the development of dashboards, reporting and clear communication help the customer visualise the impact, influence, and status of their fraud landscape so that informed decisions relating to risk, prevention and the management thereof can be made.
The overall objective is to understand and transform data into usable knowledge that delivers customer value in fraud detection and analytics while establishing the organisation as a strategic partner in national fraud prevention. The Fraud Business Intelligence Analyst will be required to identify opportunities to grow, optimize, and improve processes that contribute directly to the proactive detection of fraud and the reduction of fraud losses while minimising impact to genuine transactions. This involves the development, optimization, maintenance, and evolution of fraud data models at national level and cross border which will require you to have a broad understanding of money laundering trends and techniques
Key abilities include leveraging off of multiple skillsets, including data engineering, advanced computing, scientific methods, statistical computation, visualization (keylines, regraph and kronograph), business communication, and domain expertise.
You will engage with the following stakeholders:
- Fraud Team (Business Owner, Analytics and Detection Team, Stakeholder Relationships)
- Internal departments (IT Ops: Infrastructure, Networks, Applications, Database, Service Desk)
- Service providers, industry bodies & customers
- Reviewing, analysing, and evaluating ideas, to enhance the data and reporting deliverables for TFM
- Manage reporting on client risk profiles ensure client risk requirements (hit rates, false positive rates, alert volumes etc) are accurately measures
- Ad hoc investigations of system output anomalies and general troubleshooting
- Manage data issues relating to detection and the incorporation of new data feeds when required/available
- Data engineering support for the acquisition of quality fraud data
- Data wrangling evaluation to minimise validation errors
- Technical to business communication
- Ad hoc investigations of system output anomalies and general troubleshooting
- Contribute to department publications on actionable insight in line with proven data driven use cases
QUALIFICATION/KNOWLEDGE (INCLUDING MOST RELEVANT FIELD OF STUDY)
- MSc or BSc - Applied mathematics, statistics, computer science or equivalent
- Professional Business Intelligence or Data Analysts qualification
- Expert in MS Office
- Experienced in data analytics life cycle
- Experienced in data engineering and data wrangling
- Experienced in scripting and programming using RStudio, Python, SQL
- Experienced in fraud detection models, machine learning techniques
- Experienced in data visualisation tools and dashboard development using Qlik Sense
- Big data analytics
- Financial industry and payments fraud
- 5 years related experience
- Demonstrates excellent communication (written, oral) and interpersonal, business acumen skills
- Demonstrates problem solving and critical thinking
- Attention to detail
- Organise and manage multiple tasks
- Work autonomously and in teams
