Check Your Resume Before You Apply

Most employers use software (an ATS) to read and rank resumes. See your score and fix it. Free, no signup to check.

Paystack
Technical Financial Crime Manager
Lagos, Nigeria

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How it works

Key Takeaways

  • Automated hiring systems can screen or route resumes before human review; ResumeGeni treats ATS scoring as parser-readiness triage, not a hiring prediction (Harvard Business School & Accenture).
  • The most common ATS-readiness problems are missing keywords, incompatible formatting, incomplete fields, and incorrect file types
  • ResumeGeni scores parseability, structure, contact fields, content completeness, skills, and keyword signals, then explains the evidence behind the score

How ATS Resume Scoring Works

Applicant tracking systems parse your resume into structured data — extracting your name, contact info, work history, skills, and education — then use that data in search, review, and matching workflows. Parsing gaps can make a qualified candidate harder to evaluate.

LayerWhat It ChecksWhy It Matters
Document extractionFile format, encoding, readabilityCorrupted or image-only PDFs fail immediately
Layout analysisTables, columns, headers, footersMulti-column layouts break field extraction
Section detectionExperience, education, skills headingsNon-standard headings cause sections to be missed
Field mappingName, email, phone, dates, titlesMissing contact info is a common cause of immediate rejection
Keyword signalsJob-specific terms, skills, certificationsKeyword overlap can affect recruiter search visibility and resume-review workflows
Chronology checkDate ordering, gap detectionReverse-chronological order is expected by most ATS
QuantificationMetrics, numbers, measurable outcomesQuantified achievements help human reviewers and some scoring models
Confidence scoringOverall parse quality and completenessLow-confidence extraction means important fields may need manual review

What ResumeGeni Checks Before Keyword Matching

Keyword matching only helps after the resume can be read cleanly. ResumeGeni starts with parser-readiness signals before it evaluates wording, skills, and role fit.

  • Readable text: whether the uploaded file exposes selectable text instead of only a scanned image.
  • Standard resume structure: whether contact, summary, work experience, education, and skills sections are easy to identify.
  • Field extraction: whether names, email addresses, phone numbers, employers, titles, dates, degrees, and skills can be mapped into stable fields.
  • Format risk: whether tables, columns, text boxes, decorative icons, headers, footers, or unusual bullets could interrupt parsing.
  • Evidence quality: whether experience bullets include scope, tools, metrics, and outcomes rather than generic duty lists.
  • Keyword coverage: whether relevant tools, certifications, industry terms, and role-specific phrases appear naturally in the resume.

What Your ATS Score Means

The score is a diagnostic signal, not a hiring guarantee. A high score means ResumeGeni can extract and evaluate the resume with fewer warnings. A low score means the resume likely needs structural fixes before keyword tuning matters.

Score RangeReadBest Next Action
90-100Strong parser readiness with few visible gapsTailor keywords and achievements to the target role
75-89Generally readable, but some sections or evidence may be weakFix warnings, add measurable achievements, and tighten skills
60-74Important content may be missing, vague, or hard to mapRepair structure before rewriting bullets
Below 60Parsing or completeness issues are likely holding the resume backMove to a cleaner format and rebuild core sections first

What To Fix First

Start with problems that prevent a system or recruiter from reading the resume. Save small wording changes for after the structure is clean.

PriorityFixReason
1Use a text-based PDF, DOCX, or plain text resumeImage-only files and corrupted exports cannot be reliably parsed
2Use one column and standard headingsPredictable structure improves section and field detection
3Put contact information in the body, not only the headerSome parsers ignore header and footer regions
4Replace vague duties with quantified achievementsSpecific outcomes help both recruiter review and scoring evidence
5Mirror role language truthfullyRelevant keywords help search and review without keyword stuffing

How To Use the Score Without Overfitting

The best use of an ATS score is triage. Fix problems that make the resume hard to parse or hard to evaluate, then stop when the document is clear. Do not chase a perfect score by adding keywords you cannot defend in an interview or by turning every bullet into a list of tools.

Checker signalGood correctionCorrection to avoid
Low parse confidenceMove to a single-column layout, standard headings, and selectable text.Adding more keywords before the resume can be read cleanly.
Weak evidence bulletsRewrite duties into scope, action, tool, and measurable outcome.Inflating impact numbers or copying sample bullets that do not match your work.
Missing role termsAdd truthful tools, certifications, patient loads, stack details, or workflows from your experience.Keyword stuffing a skills section with technologies you have not used.
Thin company fitCompare the resume with the target role and company application guide before applying.Submitting the same generic version to every employer.

Methodology And Limits

ResumeGeni checks format, extraction, content completeness, and keyword signals from the uploaded resume. It does not certify that every employer ATS will parse the file the same way, and it does not predict whether a recruiter will interview you.

For the scoring rubric, privacy notes, and limitations, read the ATS Resume Checker Methodology. For the broader source map behind ResumeGeni guidance, use the research hub and dated research data dashboard. For application context, use the exact company application guide or role guide that matches the job.

What the Checker Can Diagnose

Treat the ATS resume checker as a document-readiness diagnostic, not a hiring prediction. A useful check should tell you whether the resume text can be extracted, whether the major sections are recognizable, whether contact fields are present, whether bullets contain evidence, and whether role language appears naturally enough for a reviewer to understand the match.

Diagnostic areaWhat ResumeGeni looks forBest correction
Text extractionSelectable text, readable file structure, and parser confidence.Use a text-based PDF, DOCX, or pasted text version before changing wording.
Section recognitionStandard headings for contact, summary, experience, education, skills, projects, and certifications.Rename creative headings to conventional resume sections and keep content in the document body.
Evidence qualityBullets with scope, action, tools, and measurable outcomes rather than generic duties.Rewrite the most recent role first, then work backward through older experience.
Role alignmentTruthful keywords, credentials, systems, technologies, and responsibilities that match the target role.Compare the resume with a role guide and a real posting before adding or removing terms.

Pair the Score With a Role Guide

An ATS score is the starting point. After the resume is readable, compare it with the role you are targeting so your skills, bullets, and keywords match the actual posting without keyword stuffing.

Resume pathUse this guide when the checker flagsBest next page
ClinicalMissing license, certification, patient-load, unit, EHR, or care-outcome evidenceRN resume guide
TechnicalThin stack detail, unclear shipped features, missing testing, deployment, or performance evidenceFull-stack developer resume guide or Android developer resume guide
PortfolioCase studies, client scope, shipped work, project outcomes, or collaboration signals are too vagueProduct designer resume guide or Freelancer resume guide
People operationsHRIS, compliance, hiring, retention, employee-relations, or policy examples are missingHuman resources manager resume guide

Where This Checker Fits in the Application Path

Use the checker as a diagnostic gate between drafting and applying. It is strongest when the next action is specific: fix parsing risks, rewrite vague bullets, add missing role evidence, or compare the resume against a real posting. It is weaker when treated as a hiring predictor or a substitute for role judgment.

Signal from the checkerBest next pageReason
Formatting or parsing warningsATS compatibility methodologyReview the scoring categories and limits before changing the file structure.
Weak or generic experience bulletsResume guides by job titleFind role-specific examples and replace duties with evidence, scope, and outcomes.
Missing tools, systems, or certificationsSkills guides by job titleCheck which skills belong in the resume and which should appear only when truthful.
Company-specific application concernsCompany application guidesCompare employer context, ATS signals, and open-role language before final tailoring.

Sources Used For This Checker

ResumeGeni's checker combines product analysis with public resume-writing, occupational, and structured-data references. These sources inform parser-readiness guidance; they do not certify that any employer or ATS vendor will score a resume the same way.

Frequently Asked Questions

Is ResumeGeni free?
Yes. ATS analysis, scoring, and initial improvement suggestions are free with no signup required. Full guidance and saved reports may require a free account.
What file formats are supported?
PDF, DOCX, DOC, TXT, RTF, ODT, and Apple Pages. PDF and DOCX are recommended for best ATS compatibility.
How is the ATS score calculated?
Your resume is parsed into structured fields such as contact information, experience, education, and skills. The score reflects how cleanly ResumeGeni can extract those fields plus format, content, and keyword signals.
Can ATS read PDF resumes?
Yes, but not all PDFs are equal. Text-based PDFs parse well. Image-only PDFs (scanned documents) and PDFs with complex tables or multi-column layouts often fail ATS parsing. Our analyzer will flag these issues.
How do I improve my ATS score?
Focus on three areas: use a clean single-column format, include keywords from the job description naturally in your experience bullets, and ensure all sections (contact, experience, education, skills) use standard headings.

Preferred ATS Checker Resource Spine

Built by ResumeGeni. Methodology, sources, and limitations are documented above. Last updated .

Technical Financial Crime Manager

Paystack · Lagos, Nigeria

Technical Financial Crime Manager

About Paystack

Over the past nine years, Paystack has established itself as a pioneer in African fintech with a mission to help merchants get paid by anyone, anywhere in the world. Processing over $300 million in monthly transactions, our modern payments infrastructure supports tens of thousands of notable corporations, including MTN, Bolt, and Domino’s Pizza.

As we enter a phase of accelerated growth, we are seeking a Technical Financial Crime Manager to own, design, and scale our fraud and AML detection capabilities. This role sits at the intersection of data, engineering, and financial crime operations, with end-to-end accountability for ensuring our monitoring systems are technically robust, domain-accurate, and scalable across multiple markets.

This is a hands-on technical leadership role. You will define detection logic, guide system design, and directly influence how financial crime risk is identified and managed at Paystack, while also leading and developing high-performing fraud and AML teams.

What You’ll Do

As the Technical Financial Crime Manager, you will run the day-to-day fraud and AML detection stack; from data and rules to operational outcomes. You will combine deep technical expertise with financial crime domain knowledge to design effective monitoring systems, manage domain specialists, and ensure Paystack remains a safe, trusted payments platform.

You will be accountable for:

  • The technical quality and effectiveness of fraud & AML monitoring logic
  • The operating model and performance of Financial Crime Monitoring teams

Translating risk, regulatory, and business requirements into scalable detection systems

Key Responsibilities

Technical Ownership of Detection & Monitoring
  • Define, build, test, and optimise fraud and AML detection rules, scenarios, thresholds, and models used in production systems.
  • Translate complex datasets and domain insights into actionable detection logic embedded in monitoring and alerting platforms.
  • Establish feedback loops between investigation outcomes and detection logic to continuously improve signal quality.
  • Measure and manage detection performance using quantitative metrics (precision, recall, false positives, alert-to-case conversion, loss metrics).

Maintain structured, auditable documentation of rules, logic, assumptions, and changes.

Data Analysis, Modelling & Insights
  • Analyse large, complex transactional and behavioural datasets to identify emerging fraud and AML risks across markets.
  • Design and implement statistical models, machine learning approaches, and/or time-series analysis to enhance detection capabilities.
  • Build and own dashboards and reporting frameworks tracking KPIs, SLAs, alert quality, investigator productivity, and risk outcomes.

Conduct trend analysis, root cause analysis, and deep dives on losses, typologies, and control gaps.

Financial Crime Oversight
  • Own the end-to-end fraud and AML detection domain, ensuring alignment between prevention, detection, investigation, and remediation.
  • Apply deep understanding of fraud typologies, AML/CTF risks, sanctions, and regulatory expectations to detection design.
  • Manage the Fraud and AML operational teams (specialists and first-line managers) to ensure adequate coverage, capability and day-to-day execution. 
  • Translate regulatory, partner, and audit requirements into scalable technical and operational controls.
  • Stay ahead of evolving financial crime patterns, market-specific risks, and regulatory developments across Paystack’s footprint.
Tooling, Automation & Scale
  • Partner with Product and Engineering to embed detection logic into core systems and improve monitoring, alerting, and case management tooling.
  • Drive automation initiatives to reduce manual effort, improve consistency, and enable scale without compromising control quality.
  • Identify and prioritise enhancements to monitoring platforms, workflows, and data pipelines.
  • Ensure fraud and AML tooling evolves in line with transaction growth, new products, and new markets.

Operational Excellence

  • Build and continuously improve operational processes, SLAs, KPIs, and quality frameworks across Fraud and AML teams.
  • Use data and metrics to manage performance, capacity, and outcomes, ensuring teams operate efficiently and effectively.
  • Identify gaps, risks, and inefficiencies, leading initiatives to strengthen controls and scale operations sustainably.
  • Balance speed, quality, regulatory expectations, and customer experience in day-to-day decision-making.
Cross-Functional & Executive Collaboration
  • Work closely with Product, Engineering, Data, Risk, Compliance, Legal, and Customer Operations. 
  • Influence roadmap priorities related to fraud, AML, and financial crime tooling.
  • Provide clear updates to senior stakeholders on operational performance, risks, and emerging issues

Support audits, partner reviews, and regulatory engagements as a subject matter expert.

Who We’re Looking For

Required 
  • 7+ years in financial crime roles in payments, fintech, banking, or financial services.
  • Strong technical expertise in data analysis, including advanced SQL and experience working with large, complex datasets.
  • Expert Python skills, including experience with libraries such as pandas, NumPy, scikit-learn, statsmodels, and/or model pipelines.
  • Proven experience designing, building, and tuning risk detection systems (fraud, AML, or similar).
  • Solid understanding of statistical modelling, machine learning, and/or time-series forecasting, with experience deploying models into production or operational workflows.
  • Ability to translate data insights into operational detection logic used by investigators and automated systems.
  • Experience measuring and optimising detection performance using quantitative metrics.

Strong systems thinking: able to design scalable, maintainable monitoring frameworks rather than one-off rules.

  • Deep understanding of financial crime typologies, fraud patterns, AML/CTF requirements, and regulatory obligations.
  • Experience operating within fraud, AML, risk, or compliance functions in payments, fintech, or financial services.
  • Proven experience leading and developing teams, including setting direction, coaching, and performance management.
  • Ability to balance technical depth with practical operational decision-making.
  • Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.

High ownership mindset and comfort operating in ambiguous, high-growth environments.

Preferred

  • Experience with dbt and modern analytics stacks.
  • Experience with version control systems (GitHub).
  • Experience with AI-assisted tooling or advanced analytics platforms.
  • Familiarity with monitoring platforms, alerting systems, transaction screening, and case management tools.
  • Experience working with OLTP (MySQL/PostgreSQL/SQL Server), OLAP (Redshift/BigQuery/Snowflake), and NoSQL (MongoDB) databases.
  • Industry certifications such as ACAMS, ICA, CFE, CFCS, or similar.

Why This Role Matters

This role is foundational to Paystack’s ability to scale safely. You will define how financial crime detection works at Paystack, combining strong technical systems with sound domain judgment. Success in this role directly protects customers, merchants, partners, and the broader financial ecosystem while enabling Paystack’s continued growth across Africa and beyond.

This role is open to candidates based in Nigeria, Ghana, Kenya, or South Africa