Technology & computer science case competition

Technology & Computer Science Case Competition challenges members to analyze a real-world case involving multiple areas of technology and computer science. Members present their findings and solutions to a panel of judges, demonstrating knowledge in areas such as cybersecurity, IT support, information management, networking, programming, website design, and system integration.

OVERVIEW

Division: Collegiate Event
Type: Team of 1, 2, 3 or 4 members
Event Category: Case Competition
Elements: Presentation with a Topic
Presentation Time: 3-minute set-up time , 10-minute presentation time, 5-minute question & answer time

Educational Alignments

Items Provided by Competitors: Technology and presentation items for preliminary & final round presentation, Photo Identification, Conference-provided nametag, Attire that meets the Florida FBLA Dress Code.
Items Provided by FBLA: Table for preliminary round presentation and final round presentation

2026-2027 Technology & Computer Science Case

The business entity should not be contacted for additional information.  This case competition is for educational purposes only, offering a real-world scenario to help FBLA Collegiate members develop critical reasoning and casing skills. These skills can be applied to careers like software engineering, technology consulting, and AI governance and risk, etc. This year’s case competition was created in partnership with CaseBasix. 

Overview and Background Information

Client: JPMorgan Chase & Co. 

Project: Scaling a Responsible AI Coding Assistant Across the Engineering Workforce 

JPMorgan Chase & Co., headquartered in New York City, is the largest bank in the United States and one of the largest financial institutions in the world, with roughly $4 trillion in assets and operations spanning consumer and community banking, commercial and investment banking, and asset and wealth management. Its roots trace back to 1799, and the modern firm was formed by the 2000 merger of J.P. Morgan & Co. and Chase Manhattan. Today, under CEO Jamie Dimon, it serves millions of consumers and small businesses alongside many of the world's most prominent corporations, governments, and institutional clients across more than 100 markets worldwide. 

The firm organizes its work around a few core business areas: 

  • Consumer & Community Banking - everyday banking, lending, card, and payments services for millions of households and small businesses. 

  • Commercial & Investment Bank - investment banking, markets, securities services, wholesale payments, and commercial banking for corporations, institutions, and governments. 

  • Asset & Wealth Management - investment management and wealth-planning solutions for individuals and institutions. 

  • Corporate - treasury, technology, risk, and firmwide functions that support and control the businesses. 

Behind the brand sits one of the largest technology organizations in the private sector. JPMorgan spends on the order of $18 billion a year on technology and employs roughly 55,000 technologists, including more than 40,000 software engineers who build and maintain the trading, payments, risk, and customer-facing systems the firm runs on—infrastructure that moves trillions of dollars around the world every day. Software is not a back-office function here; it is the operating core of the bank, which makes both the quality and the security of that code business-critical. 

Artificial intelligence is now a central pillar of that investment. JPMorgan reports more than 450 AI use cases in production and operates a proprietary, model-agnostic platform called LLM Suite that is used daily by more than 200,000 employees, along with an AI coding assistant deployed to its engineering teams that leadership credits with 10 to 20% productivity gains. The firm has ranked first among global banks on the Evident AI Index for four consecutive years. Crucially, this work is governed: a firmwide Model Risk Governance function and an Explainable AI Center of Excellence treat responsible, human-supervised AI as a requirement rather than an option, consistent with the model-risk expectations that regulators place on large banks. 

As a systemically important, heavily regulated institution, JPMorgan cannot adopt AI the way a startup might. Every significant use of a model must be explainable, validated, and subject to human oversight, and any tool that touches production code must meet strict standards for security, reliability, and fairness. This study focuses on how AI-assisted programming can responsibly support JPMorgan's engineering workforce—accelerating how engineers write, debug, test, document, and learn—while preserving the human judgment and accountability that the firm's regulators and customers depend on. The primary users of your proposed solution are JPMorgan's software engineers and engineering leads, with important stakeholders in its AI governance, model-risk, and cybersecurity functions. 

 

Your Task 

You have been engaged by JPMorgan Chase & Co. as technology consultants supporting its engineering and AI governance teams. Artificial intelligence is rapidly changing how software is written, tested, documented, and maintained, and JPMorgan is already operating at scale: its in-house LLM Suite reaches more than 200,000 employees, an AI coding assistant has been rolled out to tens of thousands of engineers, and leadership reports 10 to 20% productivity gains. The opportunity is real, but so are the stakes. 

JPMorgan does not have an AI-adoption problem; it has a responsible-scaling problem. In a heavily regulated bank whose code moves trillions of dollars, AI-assisted programming raises the stakes on hallucinated or insecure code, embedded bias, and over-reliance on automation.  

Leadership has asked your team to design a conceptual AI assistant or AI-supported programming workflow that helps engineers write, debug, test, document, or learn something new, while demonstrating transparent reasoning, guarding against bias and misuse, and keeping qualified humans accountable for every significant decision. The guiding principle is that AI must augment human judgment, not replace it, and every design choice should reflect JPMorgan's identity as a security-first, heavily regulated institution. 

Before you begin, take time to understand the sector you will be working in. Click here to explore what the banking industry entails and to build the context you will need to create a credible, responsible-AI recommendation. 

Ultimately, your goal is to propose a responsible, AI-assisted programming solution that strengthens JPMorgan's ability to innovate while preserving the security, transparency, and human accountability that a systemically important bank requires. 

Additional Note: Your solution should draw on JPMorgan’s publicly available information, including its LLM Suite platform, AI coding assistant rollout, Model Risk Governance and Explainable AI functions, and reported scale, and should reflect the firm’s identity as a security-first, heavily regulated institution. The AI assistant does not need to be fully functional, but it must clearly demonstrate AI decision-making, ethical considerations, and usability. 

Deliverables
Your final presentation should include: 

AI-in-Programming Research & Literacy Assessment: 

  • Research the current state of AI in programming and software development, including common applications of AI-assisted coding tools and their known limitations and risks, such as bias, hallucinated output, security flaws, and misuse. 

  • Assess the evolving role of human oversight in AI-supported workflows and the AI-literacy skills engineers need to use these tools responsibly and effectively. 

  • Connect these practices to JPMorgan’s context as a regulated bank, drawing on credible, well-documented sources. 

Responsible-AI Best Practices Survey: 

  • Survey best practices, case studies, or market research from other banks and technology organizations on the ethical design and deployment of AI systems used in programming. 

  • Evaluate approaches such as user-centered and bias-aware design, human-in-the-loop decision-making, transparency in AI reasoning, and strategies for validating and verifying AI output. 

  • Where possible, draw on expert-informed resources to ground your understanding of real-world expectations, challenges, and ethical considerations. 

Conceptual AI Assistant or Workflow Design: 

  • Identify a clear problem, task, or learning challenge a JPMorgan engineer faces that could be supported by an AI assistant, then design a conceptual assistant or workflow that helps the user solve it, make a decision, or learn something new. 

  • The design must include clear instructions for user input; the rules, logic, or reasoning used to determine outputs, including decision or selection pathways; and at least one explicit explanation of how AI is being used. 

  • The tool does not need to be fully functional, but it must clearly demonstrate AI decision-making, ethical considerations, and usability. 

Safeguards, Human Oversight & Measurement of Success: 

  • Specify safeguards that prevent bias, harm, or misuse, and a concrete plan for human-in-the-loop oversight and error checking suited to a regulated, security-first bank. 

  • Define the KPIs JPMorgan should track, for example, engineer productivity, code quality and defect rates, security and compliance incidents, and AI-suggestion acceptance and override rates, and explain how leadership would use them to know the solution is working and where to refine it. 

 Recommended Resources 

Support your findings with credible, well-documented sources. The following resources offer useful background on JPMorgan's technology and AI posture and on responsible-AI governance in regulated industries: 

REGULATIONS

State: Each chapter may enter one (1) individual or team composed of two, three, or four (2, 3, or 4) active local members. Participants must be on record in the FBLA-PBL national office as having paid dues by February 1 of the current school year.

When it has been determined that an individual or team will represent the charter at state competition, the appropriate forms must be received by the date specified in the SLC registration packet.

Eligibility

• FBLA membership dues are paid by 11:59 pm Eastern Time on February 1 of the current program year.

• Members must be registered for the SLC and pay the state conference registration fee to participate in competitive events.

• Members must stay within the official FBLA housing block of the official FBLA hotel to be eligible to compete.

• Each chapter may submit one individual or team in this event.

• On the state level, each member can compete in up to three different events in the following combinations: three objective tests OR two objective tests and one presentation OR two objective tests and one production or one objective test and two production OR one objective test, one presentation, and one production. Students may enter the Christopher Heider, Rob Kelleher, Who’s Who event, and one Chapter event (Community Service, State of the Chapter) in addition to their above chosen events.

• Only competitors are allowed to plan, research, prepare, and set up their presentations.

• Each competitor must compete in all parts of an event for award eligibility.

• Picture identification (physical or digital: driver’s license, passport, state-issued identification, or school-issued identification) matching the conference nametag is required when checking in for competitive events.

• If competitors are late for their assigned presentation time, they will be allowed to compete if the judges allow it.

• Some competitive events start prior to the Opening Session of SLC. The schedules for competitive events are displayed in the local time of the SLC location. Competitive event schedules cannot be changed.

• Participants must adhere to the dress code established by the Florida FBLA Board of Directors or they will not be permitted to participate in the competitive event.

EVENT ADMINISTRATION

• This event has a preliminary and final presentation round. If there are less than 15 teams registered, the event will proceed directly to the final presentation round.

• Preliminary AND Final Presentation

o Equipment Set-up Time: 3 minutes

o Presentation Time: 10 minutes (one-minute warning)

o Question & Answer Time: 5 minutes

o Important: Time allocations are exclusive. The presentation must begin immediately after the 3-minute set-up time concludes. Time may not be shifted between segments. Competitors will not interact with judges during the set-up period.

o The presentation is judged at the SLC. Presentations are not open to conference attendees.

o Competitors/teams are randomly assigned to sections.

o Technology Guidelines

· Internet Access: Provided (Please be aware that internet access at conference venues may be unreliable. Always prepare a backup plan in case the connection is lost or does not work with your device.)

· Presentations must be delivered using one or two personal devices (laptop, tablet, mobile phone, or monitor approximately laptop-sized).

· If using two devices, one must face the judges and the other must face the presenters.

· Projectors and projector screens are not permitted, and competitors may not bring their own.

· Wireless slide advancers (e.g., presentation clickers or mice) are allowed.

· External speakers are not allowed; audio must come directly from the presenting device(s).

· Electricity will not be available.

o Non-Technology Items

· Visual aids, samples, notes, and other physical materials related to the project may be used.

· Items may be placed on the provided table or on the judges table, if space allows.

· No items may be left with the judges following the presentation.

o Restricted Items

· Animals, except for authorized service animals.

· Food, which may be used for display only and may not be consumed by judges.

· Links and QR codes, which may be shown but may not be scanned or clicked by judges at any time.

o Research

· Information must be supported by credible, well-documented sources.

· Any use of copyrighted material, images, logos, or trademarks must be properly documented.

o Team Expectations

· In team presentations, all members must actively participate in the delivery of the presentation.

National: If competing at national, see national guidelines: www.fbla.org.

SCORING
• The presentation score will determine the finalists.
• The final presentation score will determine winners.
• Judges must break ties.
• The decision of the judges is considered final. All announced results are final upon the conclusion of the State Leadership Conference.

AMERICANS WITH DISABILITIES ACT (ADA)

• FBLA complies with the Americans with Disabilities Act (ADA) by providing reasonable accommodations for competitors. Accommodation requests must be submitted through the conference registration system by the official registration deadline. All requests will be reviewed, and additional documentation may be required to determine eligibility and appropriate support.

RECORDING OF PRESENTATIONS

• No unauthorized audio or video recording devices will be allowed in any competitive event.

• Competitors in the events should be aware FBLA reserves the right to record any presentation for use in study or training materials.

PENALTY POINTS

• Competitors may be disqualified if they violate the Code of Conduct or the Honor Code.

AWARDS

State: The number of competitors will determine the number of winners. The maximum number of winners for each competitive event is four.

National: The number of awards presented at the National Leadership Conference is determined by judges and/or number of entries.  The maximum number will be ten.