The power of data in finance: How analytics is transforming the industry

The growing role of data in finance

Data has been the driving force behind innovation in a variety of industries, but the impact has not been more palpable than in the finance sector. Traditional financial analysis has completely transformed with the power of AI, as machine learning algorithms continue to enable predictive analytics and real-time insights.

By analyzing a multitude of financial data sources, from market data to customer transactions and regulatory reports, artificial intelligence has been helping financial professionals make smarter and more effective data-driven decisions.

Are you curious about the role of data in finance? Keep on reading as we explore how financial institutions can leverage data in a variety of ways, including for decision-making, risk management, fraud detection, and customer personalization.

How data is used in financial decision-making

Data is ultimately a collection of raw information, but the real power and impact of data lies in how we use this information. By analyzing historical data, we can often identify patterns and also predict future trends. This notion is particularly useful when it comes to making important financial decisions, like who to lend money to or what stocks to invest in.

For instance, with quantitative finance and algorithmic trading (also referred to as algo-trading), AI uses past data to identify potential risks and opportunities in the market. With this knowledge, financial professionals can make better trading decisions, in turn strengthening their investment strategies and optimizing portfolio management.

As well, artificial intelligence can be used when doing risk assessment and credit scoring for clients. By using predictive analytics and credit risk models, financial institutions can identify indicators of poor credit and, in turn, make better informed decisions about how to categorize clients.

Finally, economic forecasting and financial modeling also rely heavily on data. Financial organizations utilize data to predict future economic trends, such as interest rates, inflation rates, unemployment rates, etc. All these factors can ultimately make or break an institution’s competitive advantage in the market, making clear the impact of using data and AI in financial services.

Fraud detection and cybersecurity in finance

As the financial industry deals with lots of sensitive and confidential data, AI and machine learning also play a critical part in helping to keep information secure.

For example, machine learning models can learn to detect fraudulent transactions by identifying suspicious behaviour and irregular patterns. By implementing AI to detect anomalies, financial organizations can prevent fraud in real-time and often stop it before it happens.

Additionally, artificial intelligence can also help strengthen cybersecurity in the financial sector. Large financial institutions (like banks, credit unions and insurance companies) are subject to a variety of threats that can compromise confidential information and monetary assets, including phishing attempts, malware attacks, and ransomware.

To combat these harmful attacks, many businesses use AI to identify potential threats and stop hackers in their tracks. Such preventative measures, in turn, work to improve incident response times and keep both the institution and its clients’ information safe.

Personalization and customer experience in banking

Going back to the use of risk assessment and credit scoring as mentioned earlier, it’s evident that data helps financial organizations create tailored experiences for clients. This idea of data enabling increased customization can also apply to other services in banking, such as personalized loan offers and investment recommendations.

On the other hand, data also gives power back to customers themselves, allowing them to create their own personalized financial experiences. The concept of open banking is a great example of this, which is when consumers can opt to share their data with third parties (e.g. apps, services, fintech companies) to receive customized financial advice.

While open banking is not yet available in Canada, it is in use in other countries like Australia and the United Kingdom. This could primarily be due to ethical considerations of real-time financial data sharing and the security impact of financial data management.

Since financial data is so sensitive, the idea of sharing it with other platforms raises many privacy concerns. For this reason, laws and regulations have already been put in place around the world, including the GDPR (General Data Protection Regulation) in the European Union and the CCPA (California Consumer Privacy Act) in California.

Such regulations exist to protect consumer data and set guidelines for how companies should use their customers’ data responsibly. While the conversation surrounding fintech (financial technology) and big data continues, it’s clear that open banking and other customized financial experiences are the way of the future.

The role of big data and AI in fintech

Speaking of fintech, you have probably already heard that startups (including Canadian ones like KOHO and Wealthsimple) are disrupting traditional banking. With their data-driven solutions and personalized financial services, these smaller companies have become quite popular as new generations seek alternative banking solutions.

One of the ways that fintech companies use data is in their implementation of robo-advisors and AI-powered wealth management services. By offering a more accessible and low-cost entryway into investing, fintech companies have revolutionized the notion of a financial advisor, appealing to younger and often intimidated first-time bankers and investors.

Another area in which fintechs are using modernized approaches is in their use of blockchain technology. Similar to the traditional institutions, fintechs have an obligation to keep their clients’ data safe, and since fintechs are primarily digital, their data is even more susceptible to breaches.

For those unfamiliar, blockchain refers to a digital leger of transactions that can be shared across networks. By leveraging this technology, fintech companies have been able to keep their assets safe, in turn enhancing transparency and efficiency.

Challenges and risks in financial data management

It’s clear that fintechs are adopting changes rapidly, which has completely transformed the financial industry as we know it. While such advancements are positive in theory, there is a sort of tension that arises between promoting healthy innovation and maintaining security and ethical responsibility.

For example, all financial institutions are subject to regulatory compliance and must keep data privacy top of mind. In the U.S., this means adhering to SEC guidelines, and for international organizations, complying with Basel III standards. Having legal entities like these play a role in compliance ensures that fintechs and traditional banks alike are maintaining client trust and practicing business in a responsible way.

Another challenge that exists with machine learning and AI in financial services is with algorithmic biases. Since ML models and artificial intelligence create an output based on the data they are fed, it is important that data remain as unbiased as possible. In turn, this avoids the risk of skewed financial decisions, which may perpetuate systemic inequalities that already exist.

So, what’s the best way to ensure data stays unbiased and is used in an ethical way? The answer lies in robust governance. Financial organizations should not only be responsible for creating best practice frameworks that outline how data should be used, but should also be strictly enforcing compliance with these guidelines.

The future of data in finance

When we think about the future of the financial sector, there is no doubt that data will continue to permeate. As we’ve discussed, AI and automation are playing an increasing role in financial services: from enhancing decision-making to detecting fraud and customizing consumer experiences, the importance of using data is undeniable.

Another trend that is emerging is quantum computing in financial data analysis. Although still in the early stages, quantum computing will allow for complex calculations that classic computers cannot yet achieve. This advancement could certainly lead to revolutionary changes for the industry.

As we’ve touched on, though, we predict that all financial institutions will have to further invest in their data infrastructures to keep up with all the innovations that are continuously emerging. The financial organizations that will be most successful will be the ones that strike a balance between recognizing the importance of data privacy while also being open-minded to technological change.

Lastly, to adapt to our increasingly data-driven world, we also foresee an exponential demand for tech professionals. This can include job openings for roles such as data scientists, cybersecurity experts and AI or ML engineers at both smaller startups and larger corporations.

Why data is the future of finance

Speaking of hiring, Lighthouse Labs offers a variety of different programs and bootcamps for all kinds of aspiring tech professionals! Whether you’re only learning what machine learning is or you already have some coding skills up your sleeve, there’s no doubt that data skills are becoming more and more in-demand.

If you’re interested in upskilling your knowledge of financial analytics, consider signing up for the Data Analytics Bootcamp at Lighthouse Labs. Choose from our full-time, 8-week bootcamp or our flexible, 18-week program: in either option, you’ll learn all the valuable data skills you need to know, covering topics like statistical modeling, data wrangling, and more.

Interested in the world of machine learning and AI? Check out the Lighthouse Labs Data Science Bootcamp. Choose between our full-time, 12-week bootcamp or our part-time, 30-week bootcamp.

Already got a hard-working team of data pros behind you? With Lighthouse Labs’ internal talent development solutions, you can also level up your team’s expertise. Book a call with us today and we'll find the right program that fits your organization’s unique needs!