Enhancing Fintech Security with Erlang

Build Scalable and Reliable Fintech Systems with Erlang

Enhancing Fintech Security with Erlang: Insights from HappiHacking

As the former CTO of Klarna and the founder of HappiHacking and Kindio, I've spent years at the intersection of financial systems and security. The growth of digital transactions has made fintech platforms a prime target for cyber threats. I've found that leveraging Erlang's unique capabilities significantly enhances the security, scalability, and reliability of financial applications.

In this post, I'll share insights into the unique security challenges faced by fintech companies and how Erlang can be utilized to address these challenges effectively.

The Unique Security Challenges in Fintech

Fintech platforms handle sensitive personal and financial data daily. This makes them lucrative targets for cybercriminals employing tactics like phishing, malware, and distributed denial-of-service (DDoS) attacks. New vulnerabilities are constantly emerging, and integrating modern applications with legacy systems often adds layers of complexity.

Ensuring security requires robust technological solutions and a deep understanding of regulatory compliance. Regulations like GDPR, PSD2, PCI DSS, and standards like ISO/IEC 27001 mandate strict guidelines for data protection and transaction security. Non-compliance can result in severe penalties and loss of customer trust.

Building Security from the Ground Up with Erlang

Erlang's architecture provides a robust foundation for developing secure fintech applications, and at HappiHacking, we have fully leveraged its capabilities to create systems that are both secure and high-performing. One of the key strengths of Erlang is its support for concurrency and process isolation. Erlang’s lightweight processes operate independently and communicate through message passing, ensuring that failures or data leaks are contained, minimizing the risk of cascading issues across the system.

Additionally, Erlang's focus on immutable data structures aligns well with the functional programming paradigm, reducing the complexity involved in managing concurrent operations and eliminating the risk of accidental data modifications. This characteristic enhances both system reliability and security.

Another critical feature of Erlang is its built-in fault tolerance, achieved through the use of supervision trees. These trees monitor system processes and automatically detect and restart failed processes, ensuring high availability and resilience—both of which are crucial for financial applications that demand uninterrupted uptime.

Best Practices for Securing Fintech Applications

Building on Erlang's strengths, here are some best practices we've implemented in our projects:

1. Encrypt All Data: Utilize strong encryption algorithms for data at rest and in transit. Erlang's ssl module facilitates the implementation of secure communication channels.

2. Implement Multi-Factor Authentication (MFA): Enhance user authentication by combining something the user knows (password) with something they have (token) or something they are (biometric verification).

3. Regular Security Audits: Conduct periodic security assessments and penetration testing.

4. Secure Coding Practices: Emphasize code reviews, input validation, and adherence to secure coding standards.

5. Incident Response Planning: Develop a comprehensive incident response plan.

6. Compliance Monitoring: Use automated tools to ensure ongoing compliance with relevant regulations and standards.

7. Tokenization: Replace sensitive data with tokens. In our applications, we've implemented tokenization using UUIDs to minimize the exposure of sensitive information.

8. API Security: Secure APIs using OAuth 2.0, JWTs, and implement rate limiting. Erlang's capabilities make it efficient to handle secure API requests at scale.

9. DevSecOps: Integrate security into the development lifecycle. We incorporate security checks into our CI/CD pipelines to catch issues early.

10. Blockchain Technologies: Leverage techniques from the blockchain world, such as immutable records and transparent transactions. We've explored using Erlang to implement blockchain features like Merkle trees for data verification.

11. Property-Based Testing: is a powerful tool we utilize to ensure that our systems behave correctly under a wide range of scenarios. This testing method generates a variety of inputs and checks system properties to ensure correctness and robustness.

Leveraging Erlang for Fintech Security

Secure API Development

Erlang's process isolation is ideal for developing secure APIs. Each process handles individual requests, ensuring that if one process is compromised or crashes, others remain unaffected.

Real-Time Fraud Detection

Erlang excels at handling concurrent processes, making it perfect for real-time fraud detection systems. By spawning a separate analyzis process for each transaction, we can analyze patterns in parallel to the exection of the transaction. This leads to lover latencies for transactions.

Implementing Protocols with Binary Pattern Matching

Erlang's binary pattern matching simplifies implementing complex protocols like FIX SBE.

-module(fix_sbe).
-export([encode_order/1, decode_order/1]).

-record(order, {
    order_id,
    price,
    quantity,
    side,
    symbol
}).

encode_order(Order) ->
    SymbolPadded = pad_symbol(Order#order.symbol, 6),
    Body = <<
        Order#order.order_id:64/big-unsigned,
        Order#order.price:64/float,
        Order#order.quantity:32/big-unsigned,
        Order#order.side:8/unsigned,
        SymbolPadded/binary
    >>,
    MsgLength = byte_size(Body) + 4,
    <<
        MsgLength:16/big-unsigned,
        1:16/big-unsigned, % Template ID for Order
        Body/binary
    >>.

decode_order(Binary) ->
    <<MsgLength:16/big-unsigned, TemplateID:16/big-unsigned, Rest/binary>> = Binary,
    case TemplateID of
        1 ->
            decode_order_body(Rest);
        _ ->
            {error, unknown_template}
    end.

decode_order_body(<<OrderID:64/big-unsigned, Price:64/float, Quantity:32/big-unsigned, Side:8/unsigned, Symbol:6/binary>>) ->
    SymbolTrimmed = binary:trim(Symbol, trailing, $ ),
    {ok, #order{order_id = OrderID, price = Price, quantity = Quantity, side = Side, symbol = SymbolTrimmed}}.

pad_symbol(Symbol, Length) ->
    SymbolLength = byte_size(Symbol),
    PaddingSize = Length - SymbolLength,
    Padding = << " " || _ <- lists:seq(1, PaddingSize) >>,
    << Symbol/binary, Padding/binary >>.

This code demonstrates how binary pattern matching efficiently, and more importantly in a very readable way, handles complex binary protocols.

Real-World Examples of Erlang in Action

At HappiHacking, we’ve collaborated with numerous companies to implement Erlang for secure and high-performance systems:

• WhatsApp uses Erlang to maintain secure messaging for millions, leveraging its process isolation and concurrency to ensure fast and reliable communication.

• Aeternity utilizes Erlang for blockchain compliance, employing its features to handle secure transactions and uphold data integrity in regulated environments.

• Kindio relies on Erlang's fault tolerance and concurrency to manage Euro and SEK transactions securely in real-time across the European financial market.

• Klarna processes millions of payments daily with Erlang, which provides the reliability and real-time capabilities needed for secure global transactions.

• Delta Exchange deploys Erlang for high-frequency trading, utilizing its concurrency model to execute trades efficiently and securely.

• Deutsche Telekom partnered with HappiHacking to develop a GDPR-compliant data pipeline that processes 1 billion events daily, focusing on large-scale system architecture and data security.

Erlang’s versatility extends beyond these examples:

• Vocalink (Mastercard) employs Erlang for robust financial switches powering national payment systems.

• Goldman Sachs integrates Erlang in its hedge fund trading platforms to achieve microsecond-level latency for market data processing and trading.

• Nintendo uses Erlang for its Switch console’s messaging, managing millions of concurrent connections, while Riot Games relies on Erlang to support real-time communication for millions of players.

• AdRoll processes half a million real-time bid requests per second using Erlang, optimizing ad placements with millisecond precision.

Conclusion

Security in fintech is non-negotiable. By leveraging Erlang's strengths, fintech companies can build systems that are not only secure but also scalable and resilient. At HappiHacking, we've harnessed Erlang to deliver solutions that meet the rigorous demands of the financial industry.

Explore More with HappiHacking

For tailored support in building secure, reliable fintech systems, contact HappiHacking at info@happihacking.se.

Reach out to Kindio for secure, real-time management of Euro and SEK transactions.

Check out these resources:

• The BEAM Book: A comprehensive guide to the Erlang VM.
• Designing Concurrency: The Erlang Way: Insights into building concurrent applications.
• Neural Networks in Elixir: Exploring AI capabilities with Erlang and Elixir.
• Delta: Designing Financial Systems for Performance: Strategies for optimizing financial systems.
• Aeternity Blockchain: Leveraging Erlang in blockchain technology.

About HappiHacking

At HappiHacking, we specialize in developing high-performance, secure applications using Erlang and Elixir. With a focus on the financial industry, we bring expertise in building scalable systems that meet stringent security and compliance requirements.

Visit our website: happihacking.com

Contact us: info@happihacking.se