Beyond Cloud And Edge Computing: What’s Next?

By Sushil Kumar

Cloud computing has transformed the banking and financial industries by offering cost savings and convenient scaling. Despite its numerous benefits, there are some limitations to performance and use cases. Edge computing can address some of these limitations while meeting the need for increased processing speed and data volumes. While cloud and edge computing continue to mature, emerging technologies like quantum computing and biocomputing represent the next frontier in computing power. Quantum promises exponential leaps in processing power by harnessing subatomic properties. Biocomputing leverages DNA and biological systems for efficient parallel computing. These technologies are still nascent, but the possible applications are numerous across industries, and the banking sector is no exception. They have the potential to revolutionize fraud detection, make high-frequency trading easier and faster, and create improved compliance outcomes and customer experiences. Challenges remain around the stability, scale, and practical applications of these new computing paradigms. Additionally, the increased computing power offered by these new technologies raises cybersecurity concerns. For the best outcomes, it is vital for businesses to balance investments between established technologies and emerging ones and to make crucial investments where the return on investment (ROI) is highest.

Blockchain and the decentralized computing paradigm

Concerns around data sovereignty, security, compliance, and the desire to minimize vendor lock-in have spurred interest in decentralized computing approaches in the banking sector. Blockchain technologies, which store data in distributed nodes, are best known for underpinning cryptocurrencies such as Bitcoin. Instead of placing trust in a central authority to verify transactions, blockchain shifts this responsibility to cryptographic algorithms. Blockchain technology also has the potential to bring decentralized computing to the broader financial sector. Blockchain can be applied to manage identity, maintain regulatory compliance, share data between banks, and support cryptocurrency integration.

As part of the decentralized computing paradigm, blockchain can be used in combination with cloud and edge technologies to provide secure, stable hybrid data systems. The safest solutions may involve using blockchain for sensitive data with the cloud used to support large-scale, front-end applications. Decentralized networks provide enhanced security and are ideal for regulated or sensitive data. As blockchain and distributed ledger systems mature, the synergies between centralized and decentralized systems will continue to evolve.

What is quantum computing, and what does it mean for finance?

Quantum computing deploys the principles of quantum entanglement and superposition to perform much faster calculations. It replaces the basic unit of conventional computing, the bit, with the qubit. Unlike bits, which can only store values of 0 or 1, a qubit can exist in a superposition of both states, allowing for parallel processing of information. Quantum computing can perform calculations much faster—in some cases, exponentially faster—than standard computing methods. It can transform banking by speeding up the mathematical calculations that underlie finance. The increased computing speed allows banks and financial companies to deploy more advanced algorithms for risk analysis, credit underwriting, and fraud detection applications. Integrating quantum computing could also permit increased use of artificial intelligence (AI) in banking, as the amplified speed and power of quantum computers could ease the computational burden associated with training and deploying AI models. 

While the technology is still developing, major banks are already collaborating with technology companies on quantum strategies. JP Morgan Chase, for example, has partnered with QC Ware on a “deep hedging” algorithm, which has a higher degree of complexity than traditional hedging algorithms and can represent real-world market conditions more accurately. Wells Fargo, Goldman Sachs, and Citibank have also announced partnerships with quantum research groups. The hardware side of quantum computing is not ready for widespread deployment, though experts suggest this could potentially change within the current decade. There are additional obstacles to adopting quantum computing beyond the development of hardware. Upfront quantum computing costs are high, and companies may face integration issues with existing infrastructure. Furthermore, due to the newness of the technology, it may be challenging to find employees with skills and expertise in this area. 

Because quantum computing is so powerful, it opens newer avenues of misuse. For example, hackers could use quantum computing to break cryptographics that require too much computing power to be broken under current paradigms. On the other hand, quantum computing itself can be used to create new cryptographic algorithms that are more difficult to break.

Careful investments in areas where ROI is likely to be highest can help fintechs keep up with advancements in quantum computing cost-effectively. For example, companies may choose to enter into partnerships with quantum tech companies and startups. Organizations can also participate in industry quantum computing consortiums and invest in quantum training for developers and users. Rigorous governance will be necessary to ensure a strong regulatory framework for quantum computing. Businesses need to work actively with regulators to ensure the safe development and deployment of quantum computing.

How biomolecular computing affects banking

Biomolecular computing harnesses the way biomolecules work in living organisms to perform complex calculations in a concise amount of time. DNA base pairing, for example, inherently involves rapid parallel processing, and DNA computing uses these abilities by encoding algorithms into DNA strands. In the banking and finance industry, DNA computing could allow unprecedented calculation speed on large volumes of data. DNA also has the potential as an efficient, compact, and sustainable way to store data. 

Similarly, enzymatic computing uses enzymes as logical gates to process biochemical reactions as computational steps, offering speedy, efficient real-time computing. Molecular programming creates complex molecular circuits that can replicate electronic circuits. These types of computing take advantage of the storage density of natural systems, providing hundreds of times the storage of traditional, silicone-based computing systems while using less energy with a smaller environmental impact. 

Potential applications of biomolecular computing in finance include:

• Fraud detection. Biomolecular systems can use embedded algorithms to analyze patterns in financial transactions and identify fraud.

• Risk modeling. The inherent parallelism of biomolecular computing systems enables the evaluation of multiple risk scenarios simultaneously, providing a more complex and accurate risk assessment. 

• Enhanced security. The encoding of DNA provides new, difficult-to-decrypt encryption techniques.

• Faster trades. Molecular circuits can be encoded to make financial decisions in real-time, allowing faster transactions.

• Miniaturization. The compact size of DNA computing systems enables portability and easy storage.

The development of biomolecular computing technologies is still in the early stages. Practical use cases are still a work in progress, and significant technical hurdles remain. The input and output interfaces for biomolecular computing systems are currently slow. Biological computing systems are sensitive to temperature changes and must be kept in a stable environment. The error rates for these systems are relatively high, as faulty base pairings in DNA are common. Despite these issues, the technology is promising and continues to evolve. For example, a collaboration between Microsoft and researchers at the University of Washington, Seattle, has made significant advances in storing digital information in DNA.

Emerging technologies and the future of finance

Quantum and biomolecular computing are two emerging paradigms that have the potential to change banking, just as cloud and edge computing have done. The future of banking could also involve the adoption of neuromorphic computing, in which chips modeled on the human brain offer increased efficiency for AI applications, or optical computing, in which data is processed via photons and lasers. Systems might self-manage or operate without provisioning servers, using resources on demand. The rise of decentralization represents another major shift that can increase security and privacy. 

The future of computing in the financial sector will likely involve a hybrid model harnessing the strengths of multiple approaches. Striking a balance between maintaining familiar, tested technologies and purchasing cutting-edge systems is critical for financial institutions. Investing in emerging technologies can give banks a competitive advantage and help them thrive in the changing financial landscape created by these innovations. If proper regulatory guidelines are in place to ensure that these technologies are deployed securely, ethically, and responsibly, they can create a more robust, efficient, and user-friendly banking environment.

About the Author: 

Sushil Kumar is a digital transformation expert with decades of experience in leading and scaling state-of-the-art technology solutions in the defense, banking, insurance, and airline industries. A well-recognized thought leader, he is currently managing large technology projects for county government organizations in the greater Los Angeles area. Kumar holds a Master of Technology degree and received his MBA degree at the University of Southern California Marshall School of Business. Connect with Sushil Kumar at [email protected] or on LinkedIn.