Digital transformation is on the verge of revolutionizing the financial industry as we know it. It has become a crucial moment in allowing companies to build a successful strategy and stay competitive. The demand for digital interaction and high-quality customer service allows digital transformation and Data Analytics in Finance to provide benefits such as risk assessment, personalization, and forecasting. In the Financial industry, every percentage and millisecond latency can make a difference. Thus, companies should always strive to be bold and innovative to stay competitive.
This article is going to dig deeper into how digital transformation enables Data Analytics in Finance. We’ll talk about how companies can benefit from it by successfully turning real-time data into useful insights. As a result, implementing analytics tools can impact organizations’ entire business from their processes to the decision-making and customer experience.
DIGITAL TRANSFORMATION IN FINANCE
According to the EY’s ‘Digital Disruption in Finance’ report, 92% of the surveyed companies are on their way to digitalization. However, only 11% are confident in their knowledge and digital position. And while digitalization is realized as a necessity for most organizations, some of them are still struggling to navigate the change. Reshaping an entire industry and utilizing new technologies may be daunting. Still, the impact they can have on both customer experience and revenue makes the journey worth it.
Today, customer experience is much more than customer service. Here we imply every single touchpoint and interaction customers may have with a company’s services. This includes receiving real-time notifications, getting questions answered instantly by a chatbot, or accessing accounts, reports, or statements from multiple devices. So, the higher customer satisfaction institutions have, the higher returns on revenue and profitability are, thanks to digital transformation.
By adopting digital tools and technologies, financial companies can now automate all processes that have been done manually by now. For example, services such as KYC verification, procurement orders, invoice generation, and reporting.
HOW DIGITAL TRANSFORMATION ENABLES DATA ANALYTICS IN FINANCE?
Data Analytics in Finance allows companies to process large sets of data by using already accessible algorithms and analytics methods. Implementing more digital technologies than ever, companies are now turning to Data Analytics to provide a better understanding of the collected data and therefore drive successful digital strategies as a result.
Data Analytics in Finance can be divided into areas including competitive analysis, forecasting, consumer analytics, and more. Similarly, each of them can be enabled and greatly improved with the introduction of digitalization within organizations. Indeed, companies have gathered data even before the existence of the digital world. However, today, technologies such as Data Analytics make it possible for that data to be extracted from different silos, collected, processed, and analyzed. This maximizes its full potential, rather than relying on manually collected incomplete and unstructured data, captured in advance.
USE CASES AND BENEFITS FROM DATA ANALYTICS IN FINANCE
Data analytics in Finance is part of the inevitable shift of the industry towards a digital and data-driven revolution. Once correctly executed Data Analytics tools and models will help organizations leverage high volumes of unstructured data and drive new market opportunities. As a result, this will bring value not only to the organization itself but to its customers as well. Here are some of the most valuable benefits Data Analytics brings to financial services companies.
Firstly, one of the most important benefits of Data Analytics in Finance is the access and analysis of real-time data. If there is an industry where real-time analytics are crucial, that is Finance. A sector that is rich in enormous amounts of data, extreme volatilities, and sensitive information. Thus, Data Analytics has become imperative not only for enhancing profitability and reducing customer churn but simply for staying relevant and competitive.
The ability to act on data like market prices and trading information without disrupting the user’s experience is what differentiates successful financial companies. The continued adoption of real-time analytics helps organizations face many challenges. Some of them include determining whether to extend credit based on real-time credit scoring, or which customers to target based on specific actions. Ultimately, the access to real-time data allows forecasting, personalization, and fraud prevention. Benefits, we will mention in a moment.
SECURITY AND FRAUD PREVENTION
Another crucial benefit of Data Analytics is security and fraud prevention, also known as fraud analytics. By using digital technologies fraud analytics can predict fraudulent behaviors in real-time, detect, mitigate them, and adapt to new threats. With the increase in the financial services customers do online, we also witness a rise in online fraud. Thus, now more than ever financial institutions need to heavily rely on Data Analytics and fraud management to protect clients’ data and finances.
Threats for customers vary widely from phishing, malware, account takeover, sim swapping, and more. Predictive analytics can look for patterns, make predictions, and detect potential malicious activities without disrupting the client’s experience.
For instance, to help with fraud prevention, Accedia created a banking services application for a client in the Financial services industry. The solution is based on high standard requirements regarding performance, availability, and security. Some of the main functionalities include secure sign-in and automated ad fraud detection. Additionally, it allows money transfer, balance preview, credit monitoring, and more.
Data Analytics in Finance allows organizations to get to know their customers in more detail. It shortens the distance between the client and the organization. Financial personalization is based on trust. It prioritizes customers’ financial well-being even before the business needs of the company. Therefore, this intentional decision helps organizations benefit both strategically and financially by exploiting one of their strongest assets – customer data. This includes spending habits, savings, investment opportunities, and more. Consequently, financial services companies offer the most suitable and profitable plan of action for their clients.
Personalization shows customers that banks are concerned for their well-being and put their needs first. The more customers trust an institution, the more eager they are to share personal data and receive tailored solutions. Trust is the strongest incentive in banking. It results in a more profitable relationship and a higher level of digital-enabled sales.
On the other hand, financial forecasting includes the prediction of financial trends based on real-time data. This helps to make well-informed decisions on how to use that knowledge and set apart one organization from the other. It allows setting the right strategy for products, services, investments, and so on. Additionally, organizations can advise clients on making the right financial decisions based on the implemented financial forecasting tools.
Just several years ago forecasting included the extensive hours of CFOs and financial analysts to manually gather data. However, today automated financial forecasting supports all processes within the company. For that to happen, businesses need high-quality data along with advanced algorithmic models and analytics tools. A properly gathered and purposeful data can make the difference between effective and inefficient and poor financial forecasting.
An example is the intelligent financial management platform Accedia created for a client. The goal was to help organizations analyze financial data, make projections, and timely effective decisions based on the uncovered insights. To achieve that they use the platform to upload income and cash flow statements, balance sheets, and more. Based on the information in them, the platform makes analyses, plays out possible scenarios, and creates reports. Furthermore, using real-time aggregated data, the platform advises clients on which are the most appropriate financial decisions based on the current market situation and trends.
Data analysts use on average over six different data sources, along with 40 million rows of data and 7 outputs to conduct simple data analysis. And that is for only one person and a single analysis. Once we multiply that by the number of employees and the analysis they perform in a single day, the numbers are staggering. In order to cope with the exponential amounts of complex and sensitive data, financial companies are increasingly realizing the need for Data Analytics for task automation.
Task automation makes data handling and analyzing accurate, quick, and easy, which results in higher productivity and customer satisfaction. By implementing analytics tools, financial institutions can determine whether a potential client is a liability, or whether they can offer them a certain service. On the other hand, from a client’s perspective, the automated services provide the convenience of performing almost all activities online and having fields automatically filled in with commonly used information – name, phone number, address, and more.
As part of our work, we have created a task automation platform for a leading South African financial company. The solution helps automate the preparation of financial statements, detailing the performance of different funds in the past fiscal year. The modernization and automation process allows to speed up reports generation and allows broader access to the information.
In recent years Data Analytics in Finance has moved from a nice-to-have option to a truly competitive necessity. Users are no longer limited by the old-school reporting. Having the ability to generate an in-depth comprehensive report and aggregate data from multiple channels, helps financial leaders and analysts to respond to changes and adapt promptly without risking the experience of their customers.
Indeed, these are exciting times for the financial industry. Times when people work symbiotically with data-driven models to create predictions, follow trends, analyze performance, and assess risk to enhance the financial well-being of both clients and businesses.
Learn more about our experience in Finance and how we can help you transform your business by exploring the benefits of Data Analytics.