Illustrative sample
Operational Risk Dashboard
A fictional dashboard exploring incident volume, risk ratings, action status, and monthly trends to show how metrics can support prioritization.
Risk Management | Compliance | AI & Analytics | Process Improvement
Turning Risk into
Insights and Impact
I'm Jaisell Morales, a financial services professional with experience across operational and credit risk, governance, compliance, client operations, and analytics. My work connects risk assessment, control analysis, regulatory considerations, issue remediation, management reporting, and process improvement. I’m especially interested in using data and responsible AI-assisted workflows to organize complex information, identify questions that need attention, and help teams make better-informed decisions.
✓Risk Management
▤Compliance & Regulations
▥Data Analytics
⌘SQL & SAS
▦Tableau & Reporting
✦AI & Automation
Featured projects
Practical Applications
Independent demonstrations of risk analysis, reporting, compliance thinking, and process improvement. Example figures and scenarios are fictional.
Planned independent project
Risk Data Analysis
Explore a synthetic risk dataset, compare reporting periods, identify notable patterns, and translate observations into practical recommendations using spreadsheet and visualization techniques. SQL exploration may be added as skills develop.
Planned independent project
Risk & Control Assessment
A sample RCSA for a fictional company, including risk statements, inherent risk, controls, gaps, and remediation actions.
Planned independent project
AI-Assisted Compliance Review
An independently built prototype using fictional rules and process descriptions to organize requirements and flag review questions for human validation.
Planned independent project
Process Improvement Analysis
Map a fictional workflow, identify bottlenecks, and propose measurable improvements to cycle time, documentation, and control consistency.
Planned independent project
Executive Risk Brief
A mock report turning sample findings into an executive summary, key exposures, recommended actions, ownership, and follow-up measures.
About me
Connecting risk knowledge, data, and practical solutions.
My focus is on making complex risk and compliance information clear, useful, and actionable.
I bring approximately eight years of financial services experience spanning client operations, credit operational risk, governance and controls, and compliance-focused process analysis. My work has included risk and control assessments, monitoring risk appetite metrics and fraud-loss limits, tracking issues and remediation, supporting RCSA activities, reviewing process documentation, and preparing clear reporting for risk stakeholders and leadership forums.
I approach risk as both an analytical and operational discipline. I look at how a process works end to end, how teams and systems depend on one another, whether controls address the relevant exposure, how issues are prioritized, and what evidence is needed to validate a conclusion. I value practical recommendations, clear ownership, thoughtful escalation, and follow-through rather than reporting findings without a path forward.
My technology experience includes Tableau reporting and dashboards, SAS, Jira, and SAI360/eGRC, along with exposure to SQL and AI-assisted analysis. I’m particularly interested in responsible AI applications for compliance research, process mapping, requirement organization, dependency analysis, and repeatable review workflows. AI can help structure information and surface questions, while decisions and conclusions still require human review and validation.
My areas of interest include operational and business risk, credit risk, regulatory compliance, governance, control effectiveness, consumer protection, risk analytics, AI governance, responsible automation, process optimization, and executive reporting. I enjoy work that brings these disciplines together to make complex requirements easier to understand and act on.
This portfolio is a collection of independent demonstrations, not a representation of my former employers’ systems or work products. To protect confidentiality, all sample scenarios, names, process descriptions, records, and numerical data are fictional or synthetic. No confidential information, customer data, internal documents, proprietary workflows, or restricted employer materials are included. Examples are illustrative only and should not be interpreted as actual employer results, regulatory advice, or production-ready controls.
Risk & ControlsAssessments, monitoring, remediation
AnalyticsMetrics, trends, dashboards
GovernanceRCSA, reporting, stakeholder updates
AI & TechnologyAI-assisted research, SAS, Tableau, eGRC
Professional background
Experience that informs my approach
My work has combined detailed analysis with collaboration across business and risk stakeholders.
Credit Operational Risk
Conducted risk assessments, monitored risk appetite metrics and fraud loss limits, analyzed emerging themes, and supported reporting for risk leadership and committees.
Governance & Controls
Supported RCSA activities, risk acceptance and issue tracking, control analysis, eGRC processes, dashboards, and remediation follow-up.
Compliance & Process Analysis
Reviewed regulatory requirements and process documentation, used AI-assisted analysis, and partnered with subject matter experts to validate findings.
Skills & tools
Areas of expertise
A combination of risk management knowledge, analytical tools, and process-focused problem solving.
Risk Management & Governance
- Operational and credit operational risk
- Risk and control self-assessments (RCSA)
- Risk appetite metrics, KRIs, and thresholds
- Risk acceptance, issue tracking, and remediation
- Risk trends and emerging-theme analysis
- Governance routines and committee reporting
- Action ownership and follow-up
Compliance & Consumer Protection
- Regulatory requirement and policy review
- Control design, evidence, and effectiveness analysis
- Credit reporting compliance concepts
- FCRA and Metro 2 subject-matter exposure
- UDAAP and consumer-impact considerations
- Process-to-requirement mapping
- Issue escalation and human validation
Risk Analytics & Reporting
- Risk metrics and trend interpretation
- Dashboard design and data visualization
- Management reporting and executive summaries
- Fraud-loss limit and risk appetite monitoring
- Pattern identification and prioritization
- Tableau and SAS experience
- Foundational SQL knowledge, developing further
AI & Responsible Automation
- AI-assisted research and information organization
- Process and dependency mapping concepts
- Requirement comparison and review-question generation
- Repeatable workflow design
- Human-in-the-loop validation
- Data privacy and confidentiality awareness
- AI use cases for risk and compliance teams
Process Improvement & Controls
- Process walkthroughs and documentation review
- Control-gap and dependency analysis
- Issue remediation tracking
- Workflow bottleneck identification
- Action plans and measurable follow-up
- Stakeholder collaboration
- Clear recommendations and decision support
Platforms & Ways of Working
- SAI360 / eGRC
- Jira and issue tracking
- Tableau dashboards and reporting
- SAS analysis
- SQL fundamentals
- PowerPoint and process documentation
- Structured analysis and quality review
Get in touch
Have a role or project in mind?
I'm always open to connecting with teams looking for someone who can bring together risk management, compliance, analytics, and process improvement. If you're hiring for operational risk, business risk, governance, compliance, or risk analytics, I'd welcome a conversation.
I'm also interested in collaborating on practical analytics and automation ideas that make risk information clearer and help teams make informed decisions.
LocationFlorida, USA
Areas of interestOperational & Credit Risk · Compliance · Governance · AI & Responsible Automation · Risk Analytics · Consumer Protection · Control Effectiveness · Process Improvement · Data Visualization · Executive Reporting
Portfolio noteConfidentiality: all project samples use fictional scenarios and synthetic data; no employer-confidential, customer, or proprietary information is shared.