Data Analytics in Finance: Insights and Applications

We frequently say that data science and AI-related technologies are game-changers in many sectors and industries. Well, the banking and insurance sectors are no exceptions. In fact, without data analytics in finance, modern financial companies couldn’t provide their services and offer attractive products. So yes, data finance in finance is a tremendous milestone that paves the way for the rapid development of these companies. And in this article, we are going to take a look at how Addepto can help you enhance your everyday work in the finance world.

At Addepto, we love working with financial companies. That’s because this sector fully understands the potential of data analytics in finance and utilizes it in everyday work. Take a look at some of the latest statistics provided by Forbes[1]:

• 54% of Financial Services organizations with 5,000+ employees have adopted AI
• 70% of all financial services firms are using machine learning to predict cash flow events, fine-tune credit scores, and detect fraud
• 37% of Financial Services firms globally adopt AI to reduce operational costs

These numbers tell one story–data analytics in finance is more and more broadly adopted in financial companies worldwide. And these stats also provide us with vital information on what the AI-based services are used for:

• Predicting cash flow events
• Fine-tuning credit scores
• Detecting fraud
• Reduce operational costs

Of course, that’s not the full story. We want to take a closer look at the finance sector and find out how your company can benefit from adopting data science consulting services. We decided to divide this article into two major sections. The first one is all about insurance companies. The second one will be devoted to banking companies. Let’s start with insurers and the broadly developing insurtech sector.

Data Analytics in Finance: Insurers and insurtech

Here, we want to show you three amazing ways of how data science in finance helps modern insurance companies thrive on the market.

More accurate risk assesment and pricing processes

Insurance companies are on the market not just to collect premiums and pay out compensations but also to support customers with everyday problems like, for example, a blocked sink or a leaky roof. With data analytics features, insurance companies can more effectively monitor their customers’ lifestyles, analyze their calls, and help them more effectively. Moreover, that data can be used to adjust your rates and reward reliable customers with lower premiums.

Data science in finance enables insurers and insurtech companies to assess the profile of each new customer. This way, these companies can:

1. Shorten the evaluation time and provide insurance cost within just a few minutes
2. Make more accurate premium calculations

One of the companies operating this way is the insurtech company Hippo. It’s a smart insurance app for homeowners. Interestingly, Hippo needs just 60 seconds to provide you with an accurate quote. And all you have to provide is your address.

How is that possible? Thanks to data science, of course. As you can read on their website, Hippo analyzes data coming from multiple sources to automatically answer all the relevant questions about the potential customer’s house. Their data science models use this information to generate a quote within just 60 seconds.

Improved product design and marketing

Here, the story is simple. The more data regarding your customers your company possesses and processes, the better your products and marketing campaigns are. That’s because with thorough feedback coming from the market, you can find out what your customers really want and need.

Moreover, insurers can take advantage of new sources of data (i.a., social media) to better target intended customers with specific tailor-made products, making it possible to come up with offers based on what people need not only now but also in the near future.

That’s the way Hippo works. They offer access to home-care services and ongoing home maintenance adjusted to current customer needs. And thanks to their data-driven technology, they can provide coverage for your home with just the right amount of protection, making the whole product more attractive.

And here’s another interesting example. Lemonade is an AI-fueled insurtech app. They decided to go even further with data science and built an AI bot. With this bot, you can buy a policy within seconds without the need for any paperwork or phone calls. Everything is simplified, quick, and happens entirely online. Furthermore, Lemonade leverages something they call Policy 2.0. This means that they want to keep the entire communication and documentation straightforwardly and understandably. From the customer standpoint–way to go!

Enanced claims mangement

Claims analysis and management provide insurers with vital data to prioritize claims and deal with the most complex ones later. As a result, customers with straightforward and undisputed claims can be served in the first place, making the whole process much easier and quicker. Additionally, this way, you can also improve customer experience.

Data Analytics in Finance: Revolutionizing the Banking Sector

The banking sector is definitely the branch of the financial world that makes the most of data science and AI-fueled technologies. Here, we want to talk with you about six fascinating applications:

Neobanks(aka digital banks)

We simply have to start with neobanks, sometimes also called digital banks. They are frequently referred to as Internet-only banks, which means that they have no brick-and-mortar offices or branches.

Everything customer service-related happens via the website or (mostly) the mobile application. Today, neobanks are mushrooming. That’s because they offer services to customers worldwide and provide features traditional banks lack (just to mention instant money transfers between users, attractive currency exchange and ATM rates, and online customer authentication). The list of neobanks is actually quite long, and it comprises companies like Revolut, N26, Nubank, Chime, Monzo, and several others.

In this article, we want to show you Nubank, which is the largest neobank in Brazil. They offer modern and transparent solutions to manage your money. For example, Nubank customers can enjoy the following features:

• An all-in-one mobile app
• No maintenance fees
• Free and unlimited transfers to any bank
• No credit checks in order to open an account

Improved customer targeting with data science

Just like insurance companies, banks can also utilize data analytics in finance in order to find out more about their customers and target them in a more effective way. Obviously, banks and insurance companies have access to thousands of gigabytes of consumer data.

When you think about it, they know almost everything about us. Banks know what your income is, your expenses and debts, where you eat, where you buy food, how you travel, and what you do in your free time. This knowledge can be used to make tailor-made products and services that match market demand.

Smart risk assessment and management with data science

Risk assessment is not just a domain of insurers. Banks also have to assess risk related to their customers, for instance, when they decide whether to give a loan or offer a credit card. Thanks to data science, banks can:

• Improve credit assessments
• Advance the early-warning systems

In this area of the financial world, banking companies frequently use not only data science but also machine learning. ML-based algorithms can perform a series of automated tasks like matching data records and calculating whether a given applicant qualifies for a loan or a mortgage. And we can say even more! Risk assessment using AI-powered applications and algorithms is not just quicker and more straightforward. Usually, it’s more accurate as well!

Fraud detection with data science

Fraud detection is strictly related to risk assessment, but it’s a different area from the machine learning and data standpoint. When it comes to machine learning, fraud detection is simply a classification problem. It all comes down to this–you have to indicate normal and safe operations and distinguish them from suspicious and risky ones. The next stage is straightforward.

When a questionable operation occurs, the algorithm immediately flags it for further investigation. This way, banks can act quickly and block actions that could lead to money laundering or other types of financial fraud.

In order to classify transactions as either legit or fraudulent, machine learning algorithms have to be trained on vast amounts of consumer and transactional data, especially transaction details such as the amount of money transfer, receiver, time, location, etc. And that’s the role of data science. It’s always a starting point on the way to develop fully functional ML algorithms that are capable of scanning through these vast customers’ datasets, detect unusual activities (typically referred to as anomalies), and flag them instantly.

As a result, human employees of a specific bank can quickly check this unusual activity and verify it with a customer.

Fraud detection is one of the most significant aspects of data science in finance. In the United Kingdom alone, the value of annual online banking fraud losses reached almost 160 million GBP in 2020 compared to “just” 63.7 million in 2010:

High-frequency trading (HFT)

HFT is a specific type of trading that uses powerful computer programs to transact a large number of orders in fractions of a second. With HFT, tens of thousands of transactions happen every day. The idea is simple–to make the most of price differences that might exist only for a fraction of a second.

According to Investopedia, HFT uses complex algorithms to analyze multiple markets and execute orders based on market conditions. Today, more and more investing apps and solutions support high-frequency trading. One of them is an investment app Robinhood. It’s a US-based app that allows you to invest and trade with no commissions included.

According to Bloomberg[2], Robinhood makes nearly half of its revenue (more than 40%) from high-frequency trading and payment for order flow. With this app, you can also invest in options, stocks, gold, and even cryptocurrencies.

Smart investments

That’s an application both banks and customers can benefit from. Today, more and more companies and individuals decide to invest their financial resources. However, with thousands of investment options and opportunities, it can be difficult to track everything manually. And this is where smart investment apps come into play. These apps can be used to facilitate the investment process, primarily by indicating opportunities worth pursuing.

Take a look at a company called Auquan. It’s a data science platform for asset managers and hedge funds. Auquan offers smart solutions across the entire investment workflow for portfolio managers to stay ahead of the market and achieve better returns. Their main solution is a portfolio activity monitor that allows you to discover critical as well as non-obvious news and insights that can affect your investments so that you can act before the rest of the market does.

Another interesting example of a smart investing solution is a US-based company called Betterment. They offer access to so-called robo-advisors that provide financial advice or portfolio management services. Everything happens without human assistance. The list of smart trading and investment apps and solutions is much longer, though.

As you can see, there’s quite a lot going on when it comes to data science in finance. Smart solutions currently available on the market help both insurers and banks operate in a more effective and optimized way. These solutions support both companies and individual customers in making informed decisions and act quickly. If you are interested in data science and you run a company operating in the broadly understood financial world, we encourage you to contact us!

At Addepto, we offer professional data science and AI consulting services. We help companies all over the world make the most of the data they process every day. Drop us a line today and find out more about our services!

References

[1] Louis Columbus, Forbes.com, The State of AI Adoption in Financial Services, Oct 31 2020, URL: https://www.forbes.com/sites/louiscolumbus/2020/10/31/the-state-of-ai-adoption-in-financial-services/?sh=49f4826c2aac, accessed May 5 2021.
[2] Simone Foxman, Julie Verhage, and Suzanne Woolley, Bloomberg.com, Robinhood Gets Almost Half Its Revenue in Controversial Bargain With High-Speed Traders, URL: https://www.bloomberg.com/news/articles/2018-10-15/robinhood-gets-almost-half-its-revenue-in-controversial-bargain-with-high-speed-traders, accessed May 5 2021.