JP Morgan System Design interview questions

Cracking a System Design interview at JPMorgan Chase (JPMC) is not just about technical expertise. It’s also about designing secure, scalable systems that enable $6 trillion in daily transactions. In this guide, we’ll walk you through the interview process, key System Design challenges, and actionable tips to help you stand out.

What is JPMorgan Chase?#

JPMorgan Chase (JPMC) is a financial institution and a cornerstone of the global financial system. JPMC is one of the oldest and largest banks in the world and serves millions of customers worldwide.

JPMC operates in numerous areas, including investment banking, financial services, asset management, and private wealth management. With a presence in over 100 countries and trillions in assets ($3.7 trillion, as of 2023), JPMC’s scale and influence have enabled it to lead global financial progress through service and innovation for over 225 years.

The use of modern technology like blockchain, AI-driven fraud detection, and cloud solutions allows JPMC to meet today’s complex financial demands with agility and precision.

Key takeaways

• A role at JPMorgan Chase (JPMC) is an opportunity to work at the forefront of fintech innovation using advanced technologies to solve complex problems.

• The JPMC interview process involves the following steps:

  • Pymetric test

  • HireVue interview

  • Technical interview (coding and System Design)

  • Behavioral interview

• Success in JPMC’s System Design interview lies in understanding real-world finance applications, with a focus on security, scalability, and compliance.

• In-depth knowledge of compliance requirements and data security is crucial–particularly in designing systems for the financial sector.

• As financial systems and regulations evolve, so should your understanding of System Design and its application.

Why choose JPMorgan Chase for a career as a software engineer?#

Apart from JPMC’s immense prestige, it’s also about being part of a company where technology and finance intersect to create impact. For many, it is a unique opportunity to work at the forefront of fintech innovation, using advanced technologies to solve complex, real-world problems at scale. Imagine working on a system that handles millions of transactions a day, using data and AI to enhance security, or designing seamless payment solutions for people around the globe. 

Note: With over 300,000 employees, JPMC fosters cross-cultural collaboration while providing opportunities for personal and professional growth, empowering employees to advance and excel in their careers. 

Let’s discuss why System Design is important in interviews at JPMC.

Why System Design matters at JPMorgan Chase#

Financial systems must be secure, scalable, and compliant to handle the vast transaction volume and meet regulatory standards. JPMC’s data breach in 2014–one of the largest in banking history–was a stark reminder of that need. The breach exposed information on over 80 million accounts, underscoring the importance of security as a foundational element in System Design.

Security is JPMC’s top priority, and their System Design reflects that commitment to preventing breaches. System Design at JPMC isn’t just about security and performance but also about meeting complex compliance requirements in a rapidly changing regulatory environment and seamlessly handling ever-increasing users.

Understanding the JPMorgan Chase interview process#

JPMC actively seeks talented professionals by keeping a wide range of roles open on its career page, making it easy for candidates to apply at any time. The company also conducts on-campus hiring sessions at top institutes, ensuring it attracts the best-emerging software engineers from leading universities.

Understanding JPMC’s interview process is a big part of preparing for this journey. It’s designed to find technically skilled individuals aligned with the company’s values of collaboration, innovation, and customer focus. The process can be competitive, but that’s because JPMC seeks individuals who are ready to make a real difference.

The possible interview process is as follows, which can vary based on the role you are applying for:

• Pymetric test: This is a behavioral assessment tool that evaluates a candidate’s cognitive and emotional traits through games. It helps assess traits like risk-taking, attention to detail, problem-solving, and decision-making to find the role that best fits a candidate’s strengths.

• HireVue interview: This is an online video interview platform where candidates answer pre-recorded questions. It’s commonly used in the initial stages of interviews to assess communication skills, technical knowledge through coding exercises, and personality fit.

• Recruiter’s call: After the HireVue interview, a recruiter will contact you to schedule technical interviews, either online or in person.

• Technical interview: This interview involves technical questions focusing on fundamentals of data structures (such as arrays, linked lists, hashmaps, etc.) and algorithms (that you can code in Python, Java, C++, etc.). It typically involves two to four rounds, depending on the role. For entry-level positions (internships and graduate roles), JPMC often conducts a Super DayIn super day, the candidates spend a complete day interviewing in person or via Zoom.. For more experienced hires, the technical interview typically involves at least three back-to-back rounds, either in-person or through online coding test tools like LeetCode or Hackerrank.

• System Design interview: For senior roles, you will have a separate System Design interview focusing on architecting scalable and reliable systems.

• Behavioral interview: This interview evaluates how well a candidate aligns with the company’s cultural and core values.

• Final interview: The final interview is with an HR or a hiring manager where the focus is on understanding your background and motivations. The interviewer may ask about your resume, why you’re interested in joining JPMC, and your future plans.

After the interview process, the candidate’s overall performance is analyzed, and results are announced accordingly.

The role of System Design in JPMorgan Chase interview#

The System Design interview at JPMC plays a crucial role in assessing a candidate’s problem-solving skills, scalability solutions, and understanding of compliance in real-world finance applications. Candidates are tested on their ability to design systems that handle large-scale data, optimize performance, and ensure security and compliance. This round can be impactful and sometimes a decision-maker (for senior roles) as it aligns directly with the company’s need for professionals who can contribute to innovative, secure, and scalable tech solutions in finance.

The ability to propose efficient, scalable designs can impact the level of compensation offered to candidates, as those with strong problem-solving skills may be considered for higher roles with greater responsibilities.

In a System Design interview at JPMC, expect challenges that mirror real-world issues in finance, as follows:

• Security and compliance standards: Security is paramount to financial systems, and candidates must understand data encryption, compliance requirements, and methods to protect sensitive data at rest and during transit.

• Financial infrastructure scalability: It is critical to support millions of transactions with minimal downtime. For example, JPMC’s systems must handle increased transaction loads during peak times or financial crises without a glitch. The candidate should come up with valid solutions for scalability and availability.

• Real-time data processing: Accurate real-time data is essential for functions like fraud detection and customer applications. Candidates should consider solutions that ensure data integrity across complex transactions.

In this blog, we’ll focus on designing systems similar to the challenging questions to give a comprehensive overview of how these systems work.

System Design Interview questions at JPMorgan Chase#

As discussed earlier, the System Design interview at JPMC focuses on challenges that reflect real-world issues in finance. Here are some questions inspired by real-world scenarios you might encounter in a JPMC System Design interview:

1. Fraud detection system

2. Global payment processing system

3. Data lake or data warehouse for financial data

4. Real-time market data feed

5. A machine learning system for stock prediction

Note: For all the systems related to financial services, our focus will be on the functional requirement mainly because the nonfunctional requirements like security, scalability, availability, and reliability are relatively the same.

1. Fraud detection system#

Problem statement: Design a fraud detection system that identifies suspicious transactions in real-time to ensure secure and efficient financial operations.

Functional requirements#

The system should be able to handle the following functional requirements:

• Transaction monitoring: The system should continuously monitor real-time transactions to identify unusual patterns.

• Risk scoring: The system should assign a risk score to each transaction based on factors like amount, location, and transaction history.

• Real-time alerts and notifications: The system should generate real-time alerts for transactions flagged as potentially fraudulent and notify customers of suspicious transactions.

• Halt transaction: The system should be able to temporarily halt a transaction flagged as risky.

• Fraud reporting: The system should log the details and generate reports to provide detailed insights into fraud trends.

Fraud detection System Design and workflow#

In the fraud detection system’s design, the data collector service collects real-time data from the application servers and logs it in the data ingestion layer, which consists of a real-time messaging system such as Kafka to make it available for immediate processing and Redis for caching recent data. The data is then fed to the data processing layer, which cleans and preprocess data for model use, and a batch or real-time processor extracts features such as transaction frequency, geographical patterns, and spending behaviors.

The data is then fed to the fraud detection layer, which is a central hub consisting of the rule-based system to verify and maintain rules, machine learning (ML) models to detect anomalies, assign risk scores, and an analysis of the network to identify connections between accounts. Then, a risk engine calculates risks and identifies stress models based on risk scores, rules results, and behavioral patterns.

In the end, a decision engine makes a rightful decision based on the input from the fraud-detection layer. If a transaction is highly risky, the decision engine informs the audit and reports service to log the data to the persistent layer and generate reports to provide detailed insights into fraud trends. Along with that, it informs about the decision to alert and notification service to send alerts to the fraud analysis team and send notifications to users for suspicious transactions.

2. Global payment processing system#

Problem statement: Design a global payment processing system that securely performs multi-currency online or card transactions, handling millions of simultaneous requests.

Functional requirements#

• User authentication and authorization: The system should verify users through authentication and authorization.

• Real-time payment processing: The system should process real-time transactions with low latency.

• Multi-currency support: A global payment service should support multi-currency conversion.

• Fraud prevention: The system should detect fraudulent transactions in real-time.

• Compliance and regulatory adherence: The system should be able to comply with different countries’ compliance and regulatory requirements worldwide.

• Transaction history and balance management: The system should manage the transaction history of accounts and should accurately manage and reflect the balance.

• Settlement and reconciliation: The system should be able to settle transactions and reconcile accounts daily or periodically.

• Notification and alerts: The system should generate real-time alerts for fraudulent transactions and notify customers of transaction updates.

Payment processing System Design and workflow#

In a global payment processing System Design, a user initiates a payment using a frontend banking app or merchant’s online store. The payment request, containing details like amount, currency, and payment method (along with other credentials), is then sent to a payment service. The payment service verifies the payment method (e.g., card, bank transfer) and initiates authentication (via OTP or any other secure method).

The transaction is then passed through a fraud detection serviceIt implements machine learning models to identify transaction anomalies based on historical patterns. for additional checks before reaching the payment processing engine. Here. the payment information is recorded, and if required, currency conversion is applied. A request is sent to the card-issuing bank or payment provider, where it may be approved or declined. Upon approval, the transaction is stored for later settlement, and the user and merchant receive a payment confirmation. Finally, the settlement and reconciliation service handles funds transfers and balances the amount in different accounts, ensuring that correct funds flow to the appropriate accounts.

Note: The Payment gateway API and product architecture design offers a detailed breakdown of the core requirements and inner workings of a payment system, giving you a comprehensive view of its structure and functionality.

3. Data warehouse for financial data#

Problem statement: Design a secure, scalable data warehouse to aggregate, store, and analyze large volumes of financial data from multiple sources.

Functional requirements#

The system should be able to handle the following functional requirements:

• Data ingestions: The data warehouse should be able to ingest large volumes of financial data from multiple sources, including market data, transactions, customers, employees, and partners data.

• Data storage and management: The system must securely store a vast amount of data, with encryption in transit and at rest. It should also manage metadata for organized and searchable data assets.

• Data access and analytics: The system should allow access to authorized users or services to analyze data and generate reports.

• Compliance adherence: The data warehouse should store data that adheres to compliance and regulations.

Data warehouse System Design and workflow#

The workflow of the financial data warehouse involves collecting financial data from multiple sources, such as transaction, market, customer, employee, and partner data. The data is passed through the ETL (extract, transform, load) process. It is then extracted and ingested into the system using real-time streaming pipelines, such as Kafka, to make it available for processing.

The data is transformed by processing it through a real-time batch processor. By transformation, we mean that sensitive data, such as personally identifiable information or financial data, must be encrypted or tokenized to ensure that data is secure. Moreover, it also includes formatting data into structured, semi-structured, or unstructured formats according to the nature of the data. Once data is transformed, it is then loaded to a data warehouse (for example, AWS S3) that consists of SQL, NoSQL, and time series databases, where it is stored within partitions along with metadata for easy access and scalability.

At this stage, data governance policies are enforced, applying access control, encryption, and masking to ensure data protection and compliance with regulatory standards. Different services or users access data based on access control to generate reports, detect fraudulent transactions, conduct periodic auditing, check compliance, etc., for accountability and traceability.

4. Real-time market data feed#

Problem statement: Design a real-time data feed system to aggregate, process, and distribute high-frequency financial data from multiple sources, ensuring low latency and reliable delivery.

Functional requirements#

The following are the functional requirements:

• Data ingestion: The system should be able to ingest large volumes of financial data from multiple sources.

• Data normalization: The market data feed system should normalize data by transforming, filtering, and standardizing data to ensure consistent formats across data sources.

• Data processing: The system must process and aggregate data based on relevant metrics.

• Data distribution: To rapidly serve frequent client requests, the system should cache data for quick retrieval. It should also manage subscriptions and push updates to the clients.

• Data visualization: For data visualization, the system should give role-based access to tools for visualizing market data through interactive elements like charts, graphs, reports, etc.

Real-time market feed System Design and workflow#

In the System Design of a real-time data feed, a high volume of data is ingested from multiple resources such as market data, stock exchange, APIs, third-party data providers, etc. The ingested data is then fed to the Kafka stream cluster to make it available for quick processing, whereas a Redis cache is used for quick access to recent data. The live data aggregator workers process this data in real-time by normalizing, aggregating, and applying business rules to the data.

After the aggregation layer, the data flows through a low-latency processing layer, where data is cleansed, validated, and normalized to ensure consistency and reliability. The processed data is then pushed to the caching layer, where frequent updates are served to clients. The clients can subscribe to specific feeds or data types through the Pub/Sub service, and updates, as soon as received, are pushed to those clients, prioritizing high-priority feeds (such as real-time trading data). Moreover, the system allows role-based access to monitoring and analytic tools to visualize and analyze data and generate reports accordingly.

Note: The newsfeed generation and publishing system is a similar design problem to explore that processes real-time data to generate news feeds.

5. Design a stock prediction system#

Problem statement: Design a machine learning-based stock prediction system that predicts stock prices from Reddit comments.

Functional requirements#

• Data collection: The system should be able to collect filtered data from Reddit through APIs along with real-time data from financial or stock exchange APIs.

• Data pre-processing: The system should be able to process and store raw data as well as pre-process data to classify it based on keywords such as stock and analyze sentiments.

• Feature extraction: The system should extract features such as sentiment scores, comment volumes, and engagement metrics and combine them with stock prices and indicators for modeling.

• Stock prediction: The system’s machine learning model should be trained enough to predict stock movement based on sentiment trends and time-series data.

Stock prediction System Design and workflow#

In the flow, the system first gathers Reddit comments using filters to identify relevant posts about stocks or specific tickers. The system also collects real-time stock prices from financial APIs to contextualize predictions. Once collected, the data goes through an ETL (extract, transform, load) pipeline to clean and structure it. This structured data is sent for pre-processing, where the system uses natural language processing (NLP) to pre-process text data, including tokenization, stop-word removal, and named entity recognition, to extract stock mentions and perform sentiment analysis (using VADER or BERT-based models) based on each comment’s polarity (positive, negative, or neutral). The processed data is transformed into numerical numbers, such as sentiment scores, engagement metrics, etc, using a feature extraction service.

The system’s machine learning model, usually an RNN or a transformer, is trained in sentiment and stock price trends. This allows it to make real-time stock movement predictions based on current data. The model periodically retrains itself to improve accuracy. The results are then shown through an analysis or visualization tool.

Note: You can explore Machine Learning System Design to learn concepts for machine learning System Design along with designing complex ML systems.

Tips to ace System Design interview at JPMorgan Chase#

Here are some essential tips based on interview experience from different candidates (software developers) to stand out and succeed in JPMC’s System Design interview:

• Focus on designing real-world use cases tailored to finance-specific needs like security, scalability, and compliance.

• Financial services like JPMC face strict regulations, so discuss how you’d ensure compliance, secure data, and manage audit trails.

• Showcase solutions that enhance user experience, especially in high-stakes financial systems.

• Walk the interviewer through your thought process, including trade-offs, alternatives, and why you chose one over the other, along with iterations in design.

• Last but not least, be prepared to handle edge cases as JPMC values risk assessment, so highlighting how your design handles edge cases.

Note: If you’re ready, it’s time to test your interview readiness by participating in our System Design mock interviews to gauge how well you can apply your knowledge in a real-world scenario.

Conclusion#

Preparing for JPMC’s System Design interview requires, in any case, more than technical expertise. It demands an understanding of real-world applications, scalability, security, and compliance, particularly in the finance industry. Throughout our discussion, we’ve seen that the key to success lies in designing systems that are both robust and practical.

But as we look ahead, interviewers may raise new questions, like how the increasing complexity of financial systems will shape the next generation of System Design. As machine learning and automation become more integrated into design decisions, how can we maintain human oversight without losing efficiency? And how can we make systems future-proof as financial regulations continue to evolve?

What’s next#

System Design requires continuous learning and adaptation, keeping pace with technological advancements and shifting industry needs. As key contributors in the field, we are committed to delivering up-to-date, relevant content through our courses. Explore our catalog to excel in your learning journey and stay at the forefront of the latest industry trends and practices.