Recruiting Technology and ATS: How Firms Use Software to Source, Screen, and Select Candidates

Your resume probably isn't read by a human first. At most major finance firms, an algorithm screens it before any recruiter sees your name.

Applicant Tracking Systems (ATS) and recruiting technology have transformed how firms find, evaluate, and hire talent. Understanding these systems helps both candidates and hiring teams navigate modern recruiting.

For candidates: knowing how the technology works can help you get past automated screens.

For talent leaders: understanding the tools available enables smarter technology investments and better hiring outcomes.

Here's how recruiting technology actually works in finance.

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The Recruiting Technology Stack

Applicant Tracking Systems (ATS)

The central hub of recruiting operations.

What it does:

• Stores and organizes applications
• Tracks candidates through hiring stages
• Manages communication with applicants
• Provides reporting and analytics
• Ensures compliance and documentation

Major players in finance:

• Workday Recruiting (large enterprises)
• iCIMS (mid to large firms)
• Greenhouse (tech-forward companies)
• Lever (growth companies)
• Taleo (legacy systems at large banks)
• SuccessFactors (SAP shops)

How firms choose: Large banks often use enterprise systems (Workday, Taleo) that integrate with broader HR infrastructure. Boutiques and growth firms often choose more modern, user-friendly options (Greenhouse, Lever).

Sourcing Tools

Finding candidates before they apply.

LinkedIn Recruiter: The dominant sourcing platform in finance. Recruiters search by school, employer, skills, and more.

Boolean search tools: Advanced search capabilities across databases and the web.

Talent intelligence platforms: SeekOut, Eightfold, and others use AI to identify candidates matching criteria.

Employee referral platforms: Technology to facilitate and track internal referrals.

Campus recruiting platforms: Handshake, 12Twenty, and others manage campus relationships and applications.

Assessment Tools

Evaluating candidates beyond resumes.

Technical tests: HackerRank, Codility (for quant roles). Custom modeling tests for banking.

Psychometric assessments: SHL, Pymetrics, Criteria. Measure cognitive abilities and behavioral traits.

Video interviewing: HireVue, Spark Hire. Asynchronous video responses to standard questions.

Case study platforms: Tools for administering and evaluating case interviews or modeling tests.

Interview Management

Coordinating the interview process.

Scheduling tools: Calendly, GoodTime, ModernLoop. Reduce back-and-forth on scheduling.

Interview coordination: Managing panels, rooms, and logistics.

Feedback collection: Structured interviewer feedback in one system.

Scorecards: Standardized evaluation criteria for consistent assessment.

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How Firms Actually Use This Technology

Application Processing

Resume parsing: When you upload a resume, the ATS extracts data—name, contact info, employers, dates, schools. This populates a structured profile.

The parsing problem: Creative resume formats often parse poorly. Columns, graphics, and unusual structures confuse extraction algorithms.

Keyword matching: Systems scan for relevant terms—skills, certifications, employer names. Applications without expected keywords may rank lower.

Knock-out questions: Screening questions that auto-reject candidates. "Are you authorized to work in the US?" "Do you have a bachelor's degree?" Wrong answers mean automatic rejection.

Candidate Ranking

Algorithmic scoring: Many systems generate match scores based on how well applications fit the job description.

What affects ranking:

• Keyword matches (job requirements in your materials)
• Experience duration
• Title relevance
• School/company recognition
• Recency of experience

Human review thresholds: Recruiters often only review candidates above certain score thresholds. If you're below the cutoff, you may never be seen.

Workflow Automation

Stage progression: When a recruiter moves you from "Applied" to "Phone Screen," the system can trigger automated emails.

Rejection automation: Mass rejections at certain stages. "Thank you for applying..." emails.

Scheduling coordination: Integration with calendars to offer interview slots.

Reminder systems: Nudging recruiters to follow up on candidates in pipeline.

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The Candidate Perspective

Getting Through the ATS

Resume formatting:

• Simple, clean formats
• Standard fonts
• Minimal graphics
• Clear section headers
• Avoid columns and tables

Keyword optimization:

• Mirror language from job descriptions
• Include specific skills mentioned
• Use standard terminology
• Don't "stuff" keywords—it's obvious

Complete your profile: Some systems pull from LinkedIn. Keep all professional profiles consistent and complete.

File format: PDF is generally safe. Some systems prefer Word (.doc, .docx). When in doubt, submit both if possible.

The Human Element

Referrals bypass systems: Internal referrals often go directly to recruiter queues, skipping algorithmic screening.

Networking still matters: Relationships with recruiters and hiring managers can flag your application for human review.

Email applications: Direct emails to recruiters (when appropriate) ensure human eyes see your materials.

Assessment Strategies

Video interviews:

• Good lighting and background
• Professional dress
• Look at camera, not screen
• Practice with the platform beforehand
• Treat asynchronous video like live interviews

Technical assessments:

• Practice beforehand
• Read instructions carefully
• Time management matters
• Show work where possible

Psychometric tests:

• Answer honestly (consistency is measured)
• Take in appropriate setting (quiet, focused)
• Some tests are timed—know this before starting

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The Recruiter Perspective

What Technology Enables

Scale: Handle thousands of applications without proportional headcount increase.

Consistency: Standardized processes across locations and roles.

Compliance: Documentation for legal and regulatory requirements.

Data: Analytics on pipeline, time-to-fill, source effectiveness.

Common Frustrations

False negatives: Good candidates rejected by algorithms. Resume parsing misses context.

Gaming the system: Candidates who optimize for ATS without substance. Keyword stuffing.

Technology limitations: Systems that are clunky, slow, or poorly integrated.

Over-reliance: Teams that trust technology too much and lose human judgment.

Best Practices for Talent Teams

Calibrate your screens: Review a sample of rejected applications to ensure you're not losing good candidates.

Balance automation and judgment: Use technology for efficiency but maintain human review for decisions.

Test your own process: Apply to your own jobs. See what candidates experience.

Train hiring managers: Ensure interviewers use systems effectively and provide quality feedback.

Measure what matters: Quality of hire, not just efficiency metrics.

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AI and Machine Learning in Recruiting

What's Real Today

Resume screening: ML models that predict candidate quality based on resume features. More sophisticated than keyword matching.

Chatbots: Automated initial engagement, FAQ responses, scheduling.

Matching algorithms: Suggesting candidates to recruiters based on job requirements.

Interview analysis: Analyzing video interviews for communication patterns.

What's Emerging

Predictive analytics: Forecasting which candidates will succeed based on historical data.

Bias detection: Tools that flag potentially biased job descriptions or evaluation patterns.

Skills inference: Extracting skills from experience descriptions beyond explicit keywords.

Candidate experience optimization: Using AI to personalize candidate communications and engagement.

The Controversy

Bias concerns: AI trained on historical data can perpetuate past discrimination. Amazon famously scrapped an AI recruiting tool that penalized women.

Transparency: Candidates often don't know they're being evaluated by algorithms.

Validation: Many AI claims are oversold. Actual predictive validity is often unclear.

Regulatory attention: New York and other jurisdictions are regulating AI in hiring decisions.

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Technology by Firm Type

Bulge Bracket Banks

Characteristics:

• Enterprise ATS (Workday, Taleo)
• Heavy investment in campus recruiting technology
• Sophisticated assessment tools
• Video interviewing common
• Significant volume handling

Candidate implication: Applications go through multiple automated screens. Referrals and relationship building matter even more.

Elite Boutiques

Characteristics:

• Smaller scale means less automation
• Modern ATS (Greenhouse, Lever)
• More human review earlier
• Relationship-driven recruiting

Candidate implication: Less likely to be screened out by algorithms. More emphasis on personal connections.

Private Equity

Characteristics:

• Less formal ATS usage (especially smaller funds)
• Heavy reliance on referrals and headhunters
• More informal processes
• Assessment often through case studies

Candidate implication: Technology matters less. Networks and headhunter relationships matter more.

Asset Management

Characteristics:

• Variable by firm size
• Larger asset managers use full tech stacks
• Smaller firms may use minimal technology
• Research and investment roles often have custom processes

Candidate implication: Varies significantly by firm. Research specific firms' processes.

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For Talent Leaders: Building Your Tech Stack

Assessment Framework

Before buying technology:

1. What problem are you solving?
2. What's your current process?
3. What volume do you handle?
4. What's your budget?
5. How will this integrate with existing systems?

Common mistakes:

• Buying for features you won't use
• Underestimating implementation effort
• Not involving end users in selection
• Ignoring candidate experience

Key Selection Criteria

For ATS:

• Ease of use (for recruiters AND candidates)
• Integration capabilities
• Reporting and analytics
• Candidate experience
• Compliance features
• Scalability

For sourcing tools:

• Quality of data/reach
• Search capabilities
• Integration with ATS
• Team collaboration features
• ROI clarity

For assessment:

• Validity evidence
• Candidate experience
• Bias testing
• Legal defensibility
• Ease of administration

Implementation Success Factors

Executive sponsorship: Technology implementation fails without leadership support.

Change management: Users need training and incentive to adopt new tools.

Clean data migration: Poor data quality undermines new systems.

Realistic timelines: Enterprise implementations take longer than vendors promise.

Continuous optimization: Technology needs ongoing attention to deliver value.

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The Future of Recruiting Technology

Trends to Watch

Skills-based hiring: Moving from credentials to demonstrated capabilities. Technology to assess and validate skills.

Candidate experience focus: Systems designed around candidate journey, not just recruiter efficiency.

Internal mobility: Technology to match existing employees to opportunities.

Talent intelligence: Understanding the broader talent market, not just your pipeline.

Integration and automation: Connected systems with less manual work.

What Won't Change

Human judgment: Final hiring decisions require human assessment. Technology augments, not replaces.

Relationships: Candidate experience and employer brand still depend on human interaction.

Context: Algorithms struggle with nuance, unusual backgrounds, and potential.

Trust: Candidates need to trust the process. Technology alone doesn't build trust.

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Key Takeaways

For Candidates

Optimize for systems: Clean resume formatting, relevant keywords, complete profiles.

Don't rely on applications alone: Network, build relationships, get referrals.

Prepare for assessments: Practice video interviews and technical tests before real opportunities.

Research firm processes: Different firms use different technology and approaches.

For Talent Leaders

Technology enables, doesn't replace: Use technology for efficiency while maintaining human judgment.

Candidate experience matters: Clunky systems and automated rejections affect employer brand.

Measure quality: Efficiency metrics are easy. Quality of hire is what matters.

Stay current: Technology evolves. Regularly assess whether your tools still serve you.

Balance standardization and flexibility: Processes should be consistent but not so rigid they miss exceptional candidates.

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Final Thoughts

Recruiting technology is neither magic nor villain. It's a tool that can be used well or poorly.

For candidates, understanding how systems work helps you navigate them. Get your materials in order. Optimize for parsing and screening. But remember that relationships still drive outcomes in finance.

For talent leaders, technology should serve your hiring goals, not dictate them. The best recruiting teams use technology for efficiency while maintaining human judgment for decisions that matter.

The algorithm that reviews your resume is not your judge. It's a gatekeeper. Get past it, and you still need to demonstrate value to the humans who make hiring decisions.

Technology has changed recruiting mechanics. It hasn't changed what makes someone worth hiring.