.

Data Science is the new frontier for many domains, and Finance is no exception. In this day and age, cognitive technologies are able to cope with a wide array of financial tasks. If you want to learn how to apply AI advancements to reshape your business, this article will walk you through the areas where adding AI can bring the most value. 

Data Science can enhance many business elements, but more importantly, it can give birth to a new business model and facilitate astounding growth. Let’s dive in!

Risk management automation

A vital business operation, risk management helps companies identify potential risks and mitigate their impact. Risk management has undergone significant changes over the year, giving birth to a whole new field – AI-induced risk management, where machine learning models are under the hood of business development. 

Risks refer to uncertainties in the market that can bring on threats due to failure, legal liabilities, accidents, regulators, the list goes on. Businesses can identify them early on and monitor activities to spot potential risks. That’s where Machine Learning is fit for purpose. Feeding a vast amount of data, data scientists build predictive models that largely improve risk scoring. 

Case in point, determining the creditworthiness of potential customers. Machine learning can be applied to a wide scope of operations, from digging into their spending behavior and credit history to choosing a suitable credit product. 

An example of such is Equifax, a big player in the field of consumer credit reporting. In an interview this year, Peter Maynard, Senior Vice President of Global Analytics at Equifax, claimed their new “neural network improved the predictive ability of the model by up to 15 percent.” Using it to look back at recent decisions, they found that loans that were turned down could have been made safely.

Automating risk management has huge potential, even though it’s still a nascent category. To make the most of it in a Finance organization, its leaders should add analytical skills and introduce relevant technology solutions.  

Customer data management

Data is the biggest asset of Finance organizations. If you manage it correctly, your profits will grow. Financial data sets come in different sizes and structures. It may include data from social media, transactions, historical data, and more. Even now, the lion’s share of finance specialists has to wade through tons of information without algorithms’ help. 

Without a doubt, many organizations could use the power of machine learning to glean insights from data that allow making data-driven decisions. Moreover, companies can reduce the number of routine tasks for their employees, like reporting by implementing AI technologies.

For example, JPMorgan Chase recently introduced a Contract Intelligence (COiN) platform designed to “analyze legal documents and extract important data points and clauses”. This machine learning technology allows analyzing thousands of commercial agreements in seconds and this means a dramatic reduction of the time spent on back-end processes.

Effective fraud detection

Finance organizations should maintain the utmost level of security. However, finding a solution that would ward off cybercriminals with a 100 percent guarantee can be problematic. Employing AI models calls for skillful data scientists who will be responsible for crafting anomaly detection systems to automate anti-fraud processes. A model can be trained to detect unusual transactions or activity and block them. Although building such models can be time-consuming, the effectiveness is beyond any doubt. An example of such is below. 

Danske Bank modernized its fraud detection process, which reduced its purported 1,200 false positives per day. By the time Danske Bank had finished installing and implementing the AI solution, they were able to:

• Reduce their false positives by 60% and were expected to reach 80% as the machine learning model continued to learn.
• Increase detection of real fraud by 50%
• Refocus their time and resources toward actual cases of fraud and identifying new fraud methods.

Building personal relationships with customers

AI drives personalization in Finance organizations, changing customer experience for the better and helping companies outstrip competitors in the war for customers. AI can help you offer personalized services to your customers based on their past purchase behavior and personal preferences. 

An AI tool, like natural language processing, takes personalization to a whole new level. AI-powered chatbots that guide customers to the right solution to the problem will take the load off call centers. 

Thus, implementing AI and machine learning models removes the heavy lifting, such as scheduling payments, tracking funds movement, suggesting personalized offers, and optimizing spendings.

Case in point, JPMorgan Chase. The company invested in technology and introduced a Contract Intelligence (COiN) “chatbot” designed to “analyze legal documents and extract important data points and clauses” in 2017. Manual review of 12,000 annual commercial credit agreements normally requires approximately 360,000 hours. Results from an initial implementation of this machine learning technology showed that the same amount of agreements could be reviewed in seconds. 

Algorithmic trading

One of the biggest beneficiaries of AI and predictive analytics is trading, an area where everything can change in a matter of seconds. In fact, the advent of AI has given birth to a new branch in trading, such as high-frequency trading. 

Data Scientists create models basing on recent data. That enables traders to make real-time data-driven decisions. In trading, whoever makes the right decision first stays ahead of the curve. Thus, processing new data over a short time gives the traders the edge. 

Putting together real-time and predictive models can make predictions for market opportunities more accurate. AI processes information that comes from diverse sources – news, social media, books. That allows them to understand current events and their effect on the financial markets. 

An example of AI implementation in high-frequency trading is our customer, FRST, a Blockchain analytics and strategy company. The platform offers a set of sophisticated datasets and tools designed for data scientists, developers, investors, and traders to continuously test, deploy, and share strategies. Our developers from NCube are contributing to building a system where the information about blockchain is collected in the shortest possible terms. 

Afterword

AI fuels innovation in the Finance sector and its benefits are undeniable. From reducing manual, pen-and-paper work to processing ever-changing information and enabling fast and proper data-driven decisions. Overall, Finance and AI are a perfect match, given that it generates tons of financial data that begs to be analyzed and processed quickly. Doing so, financial organizations can be fully data-driven.