FinTech

What the Convergence of Blockchain and Machine Learning Means for the Future of Finance

What the Convergence of Blockchain and Machine Learning Means for the Future of Finance | NASDAQ

By Ziqi Chen, CEO of Cortex Labs

Ziqi Chen is a machine learning specialist and the CEO of Cortex Labs, leading the charge to integrate AI and machine learning with blockchain technologies. He has been involved in a number of blockchain and crypto projects, including co-founding Waterhole.io.

Although artificial intelligence (AI) and machine learning (ML) are often conflated, they remain distinctly different technologies. Artificial intelligence refers to a machine's ability to execute intelligent tasks, whereas machine learning refers to the automated process machines use to identify meaningful patterns in data. In the absence of machine learning, artificial intelligence as we know it wouldn’t exist.

According to McKinsey, the total annual external investment in AI was between $8 billion and $12 billion in 2016. By attracting nearly 60% of this overall investment, machine learning was found to outperform all other AI market subsets. Due to its role in supporting other technologies, such as robotics and speech recognition, machine learning remains a crucial component of the artificial intelligence ecosystem.

As the uses for machine learning expand, integrations with complementary technologies will unlock even higher value. Blockchain technology, in particular, has shown immense potential when coupled with machine learning functionality. With 53% of organizations reporting AI as very important to their business success, and an equal number highlighting blockchain as critically important, both technologies remain at the forefront of digital transformation initiatives.

Machine Learning and Blockchain for Finance

In the blockchain ecosystem, the financial services industry has been one of the primary drivers of technological development. According to Greenwich Associates, the industry is spending $1.7 billion per year on blockchain as companies move past their proof of concept and on to full implementation. However, few understand the implications of introducing machine learning as a supporting technology. To better assess potential outcomes, let’s begin with analyzing the benefits and challenges associated with this emerging integration.

The Benefits of Integration

Many benefits arise from the integration of machine learning and blockchain technology. In short, the security mechanisms inherent of blockchain can be bolstered using the analytical power of machine learning. In the realm of financial services, the ability to securely and efficiently process massive amounts of data can generate immense value for institutions and end-users.

Securing Payment Networks

One of the primary benefits of blockchain technology is its ability to function as a borderless payment network. As a decentralized solution, many blockchain protocols were made to facilitate frictionless payments with low transaction costs. These solutions came about in response to the high fees and slow processing speeds characteristic of centralized banking institutions. However, despite this powerful application of blockchain technology, lingering security concerns challenge widespread adoption. 

Because blockchain transactions only require a set of public and private keys, theft and fraud are all too common. However, by leveraging the power of enhanced machine learning, inconsistencies in account activity can be easily identified, triggering human involvement. This additional layer of security protects both financial service providers and their clients. In addition, alternative AI technologies such as biometrics and behavioral analysis can further assist in eliminating security vulnerabilities.

Efficient Financial Services 

Financial institutions implementing AI and blockchain solutions are typically aiming to deliver an improvement in service speed and quality. As with any business, these institutions also have an incentive to reduce costs, thereby generating additional value. Highlighting this trend, Deloitte reports that 57% of organizations see cost savings as the primary benefit of participation in consortia blockchain networks. By combining both of these technologies to drive business processes, those providing financial services can provide greater value to customers while maximizing their returns.

Controlled Automation for Finance

The movement towards automation is undeniable, but this pursuit can pose problems if left unchecked. Without restrictions on automated processes, organizations will inevitably lose control over processes as time passes. As such, the functions of automation must occur in tandem with built-in checks and balances.

By combining blockchain-powered smart contracts with machine learning data capabilities, this dynamic becomes feasible. While smart contacts facilitate automatic processes, machine learning can simultaneously look for anomalies, triggering human involvement when necessary. This incredibly valuable infrastructure would make financial transactions completely secure, transparent, and efficient.

The Challenges of Integration

Just as there are benefits to combining these powerful technologies for financial applications, there are also inherent challenges to overcome.

Ethos at Odds

Blockchain is a decentralized technology that many see as a solution to traditionally centralized institutions, like banks. As such, some may see the use of blockchain and machine learning for financial applications as a threat to the original intent of distributed ledgers. By incorporating machine learning capabilities on a blockchain network, a digital, central “authority” begins to dictate processes.

Bad Data

Artificial intelligence, including machine learning, requires the assessment of immense data pools. And although this functionality appears ideally suited to data-intensive blockchain networks, issues arise when you begin with bad data. Whether highly irrelevant, corrupt, or suffering from sparsity, bad data can undermine the effectiveness of machine learning technology. These inputs can result in AI that misses unexpected patterns or learns entirely wrong patterns - an unacceptable scenario in the world of finance. And although there are ways to combat bad data, it remains a considerable hurdle.

Integrations of the Future

As artificial intelligence continues to generate robust investment, machine learning appears poised for significant growth. Due to its adaptable nature, the technology is likely to permeate a growing number of industries across countless use cases. The financial services industry, in particular, is well-positioned to unlock immense value through blockchain and machine learning integrations. By enhancing security, improving performance, and controlling automation, these complementary technologies drive transformative change in the financial industry. 

However, to realize this potential, the original intent of blockchain technology must remain top of mind. In addition, quality data needs to drive machine learning applications in order to maintain effectiveness. Although uncertainties remain, these technologies are here to stay together, or apart. In a constantly evolving financial landscape, blockchain and machine learning integrations may be the next drivers of transformative change across the industry.