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You will be part of the new Integrated Fraud Management (' IFM') Division within Risk Management, in the second line of defence at UOB Singapore. A new Division, initial IFM set-up will require a hands-on approach.
Based in Singapore, this role supports UOB Group to roll out the fraud analytics portion of IFM's Strategy and Target Operating Model (" TOM"), which has the Group mandate to:
drive collaboration between functions
take on a strategic role in proactively managing frauds within the Bank
put in place appropriate strategies, resources and risk assessments to mitigate regulatory, financial and reputational implications associated with fraudulent activities
as the 2nd Line of Defence, establish effective structure avoiding duplication across all 3 Lines of Defence
Analyse big data collected by the organisation on fraud, and interpret to support management's next risk mitigating decision
Build reporting dashboards and roll out to all Subsidiaries
Project manage Integrated Fraud Management initiatives and systems that involves the analysis of unstructured/big data (collectively hereafter 'IFM Projects')
Understanding the problem statement of IFM Projects and presenting technical approaches that meet the target operating model of IFM, for management approval
Coordinate with internal stakeholders on the technology aspects of the IFM Project life-cycle, across all the stages of the work process such as data sourcing, pulling, analysis and storage, through to implementation and production
For external IFM Projects, liaise with external vendors from problem statement inception to integration into UOB platform for production
For inhouse IFM Projects, develop logic and predictive models with advanced machine learning algorithms using Bank compliant methods
Write coherent reports and insight to industry developments that involves the analysis of big data to support IFM's target operating model
Constantly review and improve existing data analytical models and systems by building new variables
Other ad-hoc projects as required by the function
Excellent oral and written English
Degree or higher in Computational Finance / Mathematics / Computer Engineering or equivalent
Minimum 5 years of relevant technical experience in modeling / quantitative analysis in a risk control environment
Experience with common programming languages and systems
Solid background on unstructured data analysis, scenario building and rule calibration
Experience with computer forensics data analysis a plus
Good team player who possess drive, initiative, have an eye for details and able to work independently under pressure
Willingness to learn new skills and initially be hands-on as the Division is being built
Priority given to candidates with fraud or risk management related experience