Key Takeaways

  • 75% of U.S. employers use automated applicant tracking systems to screen resumes before a human reviews them (Harvard Business School & Accenture, 2021)
  • The most common ATS failures are missing keywords, incompatible formatting, and incorrect file types
  • ResumeGeni scores your resume across 8 parsing layers — modeled on the same steps enterprise ATS platforms like Workday, Greenhouse, and Taleo use to evaluate candidates

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 score how well that data matches the job requirements. Many ATS rejections happen because the parser couldn't extract critical fields, not because the candidate wasn't qualified.

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 matchingJob-specific terms, skills, certificationsKeyword overlap affects recruiter search visibility and ATS scoring
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 parses get deprioritized in results

Frequently Asked Questions

Is ResumeGeni free?
Yes. ResumeGeni is currently in beta — 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 processed through an 8-layer parsing pipeline that extracts structured data the same way enterprise ATS platforms do. The score reflects how completely and accurately your resume can be parsed, plus how well your content matches common ATS ranking criteria.
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.

ATS Guides & Resources

Built by engineers with 12 years of experience building enterprise hiring technology at ZipRecruiter. Last updated .

Fraud Data Analyst

Amarbank · Helsinki

Who are we?
Amar Bank is one of the most technologically advanced digital banks in Indonesia. Our leading digital lending product, Tunaiku has the distinction of being the first FinTech product in Indonesia. We are also the first digital bank on the cloud. As the first profitable digital bank, we managed to get listed on the Indonesian Stock Exchange. 

How did we manage to do that?
We are changing people’s perception of a bank. We believe we are the innovators who combine customer focus principles with creating technology-based impact. We incorporate freedom and flexibility as part of our startup working culture DNA to encourage innovation in creating better financial solutions for the banking industry. We think of ourselves as, ‘A technology company with a banking license’. For this reason, we ‘Act like a FinTech, and think like a Bank.’ 

How did it all start?
Founded on March 15, 1991, in Surabaya as PT Anglomas International Bank (Amin Bank), the bank was acquired by Tolaram Group and transformed to PT Bank Amar Indonesia (Amar Bank) in 2014. It has then undergone a significant digital transformation to become one of the country's forerunning fintech institutions through its award-winning digital lending platform, Tunaiku. 

Our philosophy, mission, and vision
Technology must impact lives, must improve lives. We exist to provide banking to those who ‘need’ and not only to those who ‘want’. Services when provided to those who need at the time of their need brings smiles. Our vision is to bring 200 million smiles.  

More about the bank with startup culture environment
Consist of 1000+ people, you will meet people who love to grow, dream big, and actually have fun at the workplace! We provide a great working environment that pushes people to grow outside their comfort zone. People with high drive and ambition find us a very attractive place to work as their career growth matches their own drive and not any staid policies. Thus we hold the honor of being awarded “Best Place to Work in Indonesia”.Recently Amar Bank was awarded as Inspirational Brand from APEA (Asia Pacific Enterprise Awards) 2022. Of course, our innovation won't stop here. So if you would love to be a part of it, have a growth mindset, and are constantly hungry for challenges, we invite you to join us in our journey to ‘Impact Lives’. 

Join us today and create #unlimitedinnovations!

Key Responsibilities

  • Data Instrumentation: Collaborate with Product and Engineering to define requirements for capturing fraud signals.
  • Analysis & Detection: Identify anomalies, suspicious patterns, and credit fraud indicators like manipulated documents or synthetic identities.
  • Investigation: Conduct deep dives into high-risk users and transactions to uncover new fraud typologies.
  • Logic Implementation: Translate insights into fraud detection rules, monitoring systems, and risk models.
  • Requirements

  • Years of Experience: Minimum of 5 years in fraud-related roles within Financial Services (Banking, Fintech, or Digital Lending).
  • Education: Bachelor’s degree in Finance, Engineering, Data Science, Statistics, Computer Science, or a related field.
  • Skills: Proficiency in identifying patterns and anomalies in behavioral and transactional data.
  • Technical Knowledge: Understanding of fraud typologies (identity fraud, shell entities, document manipulation) and familiarity with querying/visualization tools.
  • Collaboration: Ability to work across teams (Product, Risk, Engineering) and communicate recommendations clearly to stakeholders.
  • Bonus Point

  • Experience in credit fraud or trust & safety analytics.
  • Work history involving device data, behavioral signals, or real-time monitoring tools.
  • Experience contributing to fraud models or scoring systems.