Digital Transformation in Financial Services is Now a Realistic Goal !
Businesses in the Financial Services Industry (FSI) continue to focus on being holistically digital and establish themselves as customer-facing enterprises in order to gain market-leadership. But have they arrived yet?
According to a recent Deloitte report, the financial services industry is currently on the verge of a remarkable digital transformation. A staggering 90% of respondents agreed or strongly agreed that digital technologies are causing significant or moderate disruption in the industry. However, the study also found out that the majority of financial institution personnels who polled for the same do not believe that their companies are prepared for this disruption. Only 46% of FSI respondents believe or strongly agree that their companies are adequately prepared to withstand this digital disruption.
Finance
Digital transformation in financial services is now a realistic goal because of the widespread availability of business data; ability of the teams to process large sets of data using present day algorithms and analytic methods; and lastly, advancements in connectivity tools and platforms, such as sensors and cloud computing in financial services.
The current tools in Data Analytics for the Financial Services Industry are rather insufficient. Banking and financial institutions are facing increasing challenges in the form of competition, legal limits, economic pressure, and a demanding consumer base.
But, the sheer volume of data collected in today’s time exceeds the capabilities of traditional financial analytic systems and solutions. Before it reaches data scientists, equity researchers, or quantitative analysts, the data is pre-aggregated. Advanced statistical techniques can fill out critical findings that may aid banks and financial institutions in accelerating growth, optimizing productivity, and improving risk management.
As guardians of key data, the finance function is required to generate predictions and be able to support strategic plans and choices. It should ideally include data on sales, order fulfilment, supply chains, customer demand, regulatory requirements, and business performance, as well as handling real-time statistics.
Banking
Many banks commit the mistake of launching a slew of independent digital initiatives, which eventually fail due to their lack of support and coordination, which is all-essential to compete with digital-native solutions. Moreover, digital transformation in financial services must be implemented from the top down, combining digital systems, customer experience platforms, apps, and infrastructure.
The consumer lies at the heart of any transformation strategy. With interest rates near 0%, banking fees dropping substantially, and consumer demands soaring, financial institutions must optimize their big data to automate business processes and cut down expenses. Banks can swiftly develop omnichannel products, services, and capabilities by updating their applications using artificial intelligence, cloud technology, and automation. This improves user experience and instills trust and loyalty.
Our technology provides a platform to banks and financial institutions to speed up financial analytics across their full data set. Data scientists, quantitative analysts, decision-makers and other data analysts in the banking sector might be able to visualize billions of data rows numbering up to billions even and then conduct complicated queries within nanoseconds. Simply put, obtaining results for visualizing financial data is just a few mouse clicks away. They can detect inflections or trends in banking or markets while they occur, and then smoothly zoom them down to a specific time or location to find abnormalities or banking fraud.
Financial Analytics
Finance analytics cover a lot of ground– forecasting, budget reviews, and competitive analysis, to name a few— and each discipline can be further optimized digitally to improve the way financial data is collected and processed. This technique entails removing data automatically from silos and centralising it in a cloud data warehouse or any other location where it can be evaluated efficiently. This will aid in tasks such as to make data-driven decisions on the go or to generate insights from recognised income channels.
Finally, digital transformation in financial services will enable high-value projects such as dynamic process modelling, predictive performance analytics, and enhanced machine learning for more accurate forecasting. By ensuring that you have the most updated data in your warehouse, automated data integration shall also enhance revenue forecasting. It also helps in verifying the accuracy of your balance sheets and income statements, instead of manually retrieving transactions, which causes a substantial time lag. Hence, you would be able to access and evaluate them in near real time with great ease.
Fraud Detection and Analysis Using AI and Machine Learning
Nowadays people like to purchase things online which they earlier had to to buy from physical stores, such as furniture, groceries, and apparel. It may tend to become tedious to detect fraud in such a dynamic global corporate setting with an overwhelming amount of traffic and data to monitor. Fraud detection is a good machine learning application, with a great track record of success in industries such as banking and insurance.
Using artificial intelligence to detect fraud has helped firms improve internal security and streamline corporate operations. Because of its increasing efficiency, artificial intelligence has emerged as a crucial instrument for avoiding financial crimes.
AI can be used to analyze massive amounts of data in order to find fraud tendencies, which can then be used to detect fraud in real-time. When fraud is suspected, AI models can be used to reject transactions or flag them for additional investigation, as well as grade the likelihood of a fraud, allowing investigators to focus on the most suspicious cases.
Machine learning refers to analytic methodologies that ‘learn’ patterns within datasets without the intervention of a human analyst. Consider machine learning as a way of developing analytic models, and AI for their application. Machine learning, when done appropriately, can distinguish between lawful and fraudulent behaviors while also adapting to new and previously unknown fraud strategies over time.
