AI and Big Data Analytics Are Reshaping FinTech

 - AI's Role in Digital Transformation for Financial Services

Artificial intelligence (AI) will have a major impact on financial services, proving to be its most disruptive force. Financial firms, from major Wall Street firms like Morgan Stanley to robo-advisors and start-ups, are studying how tools like algorithms, data mining and natural language processing can help. The results are already evident as global banks close branches and lay off thousands of financial services workers.  

Historically, the fintech industry has been one of the first to adopt AI. Today, AI is becoming the main driver of the digital transformation of traditional finance and the gold standard for fintech services. Artificial intelligence and data analytics go hand in hand, and emerging technologies such as machine learning, neural networks and natural language processing continue to improve the data processing capabilities of financial industry players.  

With the fierce competition in fintech, businesses must offer truly valuable products and services to stand out. The business that provides the best user experience usually wins. The development of artificial intelligence may offer something extra, especially if it can promise to eliminate guesswork and human error in finance. Today, customers interact with banks and financial institutions through many different channels, which has led to an explosion of customer data collected by these organizations. This data can be effectively harnessed using AI to gain insights into current and future customer behavior.  

Fintech business areas actively leveraging AI include: generating new revenue streams through the introduction of new products and services, process reengineering and automation, risk management, customer acquisition. However, leaders in AI adoption have invested heavily in the digitization of customer service, making it a priority for AI and analytics implementation.

- The World’s Most Valuable Resource Is No Longer Oil, But Data

In the digital age, data is one of a company's most valuable resources. Banks have vast amounts of customer data that has the potential to bring real value to customers, allowing banks to better understand their needs. Furthermore, data is the lifeblood of artificial intelligence. "Data access" plays a central role in the scope and impact of AI systems. Data, and the various rules and processes that support and regulate access and use of that data, are at the heart of disruptive fintech businesses. Even the most advanced and intelligent algorithms and models are useless without efficient, secure and legal access to detailed, accurate and up-to-date datasets.  

Machine learning allows software programs to analyze large amounts of unstructured data. One way this could help bankers is by improving fraud detection. Traditional fraud monitoring systems rely on specific impersonal rules (such as geography) to detect fraudulent transactions. Machine learning can be used to analyze each customer's transactions, flagging those that don't fit their normal habits. This improved analytical capability has the potential to provide banks with insights that allow them to develop better credit models and identify risks more accurately. However, the power of big data is highly dependent on the quality of the data, which is not always readily available.  

Today, bank customer data is often unstructured — stored in systems that are inconsistent and may not communicate with each other. A customer may have multiple accounts at a bank, all in different systems with inconsistent identifiers. Many banks, as well as core processors, are struggling to coordinate these systems. Some are working on building additional data warehouses to aggregate disparate customer data to create a unified customer view. As these customers continue to interact with their bank digitally, a complete digital view of a customer can help the bank better understand and serve that customer. Banks started offering value-added services to customers, providing them with more information about these users.

- Data-driven Cybersecurity Becoming Mainstream

One of the effects of the digitization of the financial industry is the increasing number of security threats. To protect customer data and financial integrity, financial industry players will invest more in robust data-driven security systems based on machine learning.

Data-driven cyber (DDC) is the use of data and scientific methods to make more evidence-based cybersecurity decisions. It seeks to turn data into empirical evidence, taking cybersecurity decisions beyond anecdotes and intuition (which are prone to bias). 

The history of evidence-based practice in other fields, such as medicine, shows that DDC can significantly improve outcomes, transparency, and trust. DDC requires high availability, high quality and timeliness of data. It relies on skill people to: 

• design, manage and analyze data
• develop and maintain infrastructure (including any components that use machine learning or artificial intelligence)
• communicate the findings

The goal of DDC is to generate actionable insights from data. Sharing these insights across organizations can provide better situational awareness and enable collective defense. 

Establishing DDC infrastructure and processes can take time, but if designed in a modular, flexible manner, organizations can adapt to changes in technology and evolving threats. Analysts can continuously improve their skills and artificial intelligence can be upgraded to keep up with the changing situation.