Financial Risk Analyst, Advisory Services at NCBA Group

Job Purpose Statement

• This role is responsible for evaluation and identification of the areas of potential financial risks threatening Loop DFS Group assets, earning capacity, or success of the organization, by performing financial analysis and validation activities with the objective of maximizing profits and asset growth, whilst minimizing operating losses and other risk exposures.
• They are also responsible for developing business intelligence dashboards to inform decision-making and facilitate risk monitoring, and provide financial modeling to assess and predict profitability and performance of consumer, merchant and corporate products and/or services. They are also responsible for creating financial reports based on the impact of predictive modeling assumptions as well as developing various financial models and providing cost-benefit analyses for business and strategic opportunities, market share, and profitability.
• The role is responsible for risk identification, assessment and prioritization of potential risks in an agile set up. In addition to executing underwriting operations, including evaluation of credit risk proposals and recommending credit decisions and conditions for success for the consumer and merchant businesses across all markets where Loop DFS is represented, in a manner that drives and optimizes lending margins in alignment with the Group’s Risk Appetite and Business objectives.
• SME in market research, financial and credit risk underwriting process design and assessment techniques in a highly digital and data driven environment, and will assist Senior Manager to guide other Loop DFS Units in their roles and responsibilities in a manner that optimizes business margins.

Key Accountabilities (Duties and Responsibilities)

Financial 30%

• Establish and review risk tolerance thresholds based on Loop DFS strategy, Risk Appetite, manage and escalate exceptions to guard the Group against unacceptable financial and credit losses arising from breach of KRIs
• Generate and review risk measures including Cost of Risk and NPL levels
• Generate and review income simulation to ensure the Group’s risk/return/dividend targets and goals are met; and provide support for model and report enhancements.
• Monitoring covenant compliance and account performance to ensure risk stays within agreed thresholds. 
• Monitor key portfolio performance trends to ensure strong credit portfolio quality with a good balance of risk and reward to optimize business opportunities, and in line with business strategy and risk appetite. 

​​​​​​​Internal Business Processes 50%

• Conduct continuous market, economic, environmental, social and governance (regulatory) research to assess and advice the severity of financial risk within the Group 
• Establish strategies for managing risk exposure by identifying potential problems or opportunities and developing solutions.
• Validate and refine various financial and credit policies/processes through use of analytics
• Analyze risks associated with new products or services to ensure that they are financially viable – 
• Present ideas as outcomes of risk evaluation exercises via reports and presentations, outline findings and make recommendations for improvements.
• Support new business initiatives through use of quantitative (statistical) using software such as R, Python and qualitative analysis to evaluate risk.
• Conduct periodic evaluations and quality assessments to ensure that the Group’s financial systems and processes are functioning properly
• Identify and articulate inherent risks associated with lending credit to consumer and merchant businesses. 
• Using background due diligence to evaluate credit risk, and propose creation of customized credit products to tailor needs of the Business and Customer segments 
• Interact directly and maintain good rapport with Business teams and customers teams to deeply understand their business models while protecting the Group’s interests and following compliance protocols

Customer 10%

• Support New Business initiatives & proposals for incorporation into product programs
• Engage Business partners in regular asset quality reviews with provision of analytics insights to identify risks/ opportunities and recommend relevant actions
• Support review of credit policies, scorecards and program risk acceptance criteria regularly, with analytical insights of portfolio trends & performance
• Drive portfolio management, collections and recoveries initiatives through the credit cycle management framework by jointly working in partnership with business, product managers, data science team, operations team and collection teams
• Monitor key portfolio performance trends to ensure strong portfolio credit quality with a good balance of risk and reward to optimize Business opportunities and in line with business strategy and risk appetite.
• Support scorecard monitoring and analysis; involve in the scorecard development, back testing as well as validation of the score cut-off.

​​​​​​​Learning & Growth 10%

• Self-development: engage in self-development initiatives and trainings that equip staff on emerging global Risk management practices
• Subsidiary staff growth and transfer of best practices skills through regular training and coaching 
• Internal Staff growth through coaching and training 

Job Specifications

Academic:

• University degree preferably in Economics, Business Administration or Finance related fields.
• Professional qualifications such as FRM, SCIR, FRR, CPA, CISA and PMP will be an added advantage.
• Proficiency and Practical experience in data analytics and use of relevant MIS, BI & MS Office applications and statistical computer languages e.g. SQL, R, Python. 
• Strong IT skills and experience with office automation tools

Professional:

• Strong financial modelling, analytical and problem-solving skills with an emphasis on data science and risk management.
• Experience working with and ideation and creation of data architectures.
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.