In the global economy today, companies create and capture information in trillions of bytes from internal operations, transactions with customers, and suppliers. Networked sensor devices, often called the Internet of Things, like smart meters, smartphones, automobiles, machines, computer sense, create, and transfer unimaginably vast amounts of information. Humans too are generating big data available today from social media sites, consumer devices (such as personalized computers), and smartphones.
What is Big Data?
Big data refers to datasets whose size is beyond a typical database software’s ability to capture, store, manage, and analyze. There is no particular size threshold for data to be qualified as big data , such as a specific number of terabytes. The definition of big data varies with the sector depending on the capabilities of available software tools in a particular industry.
Big Data Analytics
Big data analytics involves analyzing and extracting the information systematically from large or complex big data using processing applications and software tools. It examines data to extract hidden patterns, correlations, systems, and other insights from big data. It helps businesses with identifying new business opportunities and increasing operational efficiency. Faster and better decision making are key advantages of adopting big data analytics.
Application of Big data Analytics in the BFSI sector
The Banking and Financial Services (BFSI) sector comprises a significant portion of the global economy and includes all banking, securities and capital markets, asset managers, non banking lenders, and insurance financial institutions.
Big Data helps businesses to leverage new data sources to make data-driven decisions. It helps understand growing markets, target key stakeholders, customers, and turn businesses’ big data into solutions.
The BFSI sector is adopting new technologies such as data analytics to incorporate autonomous processes, automated claims, and automated decisioning. The sector needs to analyze the risk involved in FinTech and InsurTech businesses. It is crucial to carry out predictive analytics of fraud, risk monitoring, and other related actions in collaborating with clients, suppliers and various other businesses. For example, analytics assists in evaluating fraud risk and compliance risks before executing transactions.
Innovation Beyond Big Data: What’s Next?
Digital transformation has simplified many business processes, improved service, and quality user experience for consumers. The banking sector is experiencing efficient services for clients due to digital transformation, leveraging autonomous techniques, automated underwriting, automated claims, automated decisioning, and risk monitoring. Though present technology has transformed the fundamental workings of the traditional BFSI sector, technologies such as Artificial Intelligence (AI) and Machine Learning (ML) provide benefits beyond big data analytics.
AI and ML bases systems possess self-learning ability with an increasing amount of data. ML is capable of learning new languages, patterns, systems, and techniques automatically with time. ML algorithms improve with more usage and exposure to data. Thus, AI and ML are gaining popularity in almost every industry segment, BFSI is not an exception!
Some of the uses of ML in BFSI are algorithmic trading, predictive analytics, fraud detection, AI-driven support, back office AI support, and data-driven decision management. The key difference between ML and even slightly older technologies like big data analytics is that ML especially Deep Neural Network (DNN) based systems learn from data with minimal supervision, and provides automation of processes and deep insights without having to have ‘handcrafted’.
Getting business market insights and predictions helps managers strategize the next business move towards growth. AI and ML support businesses by applying a data-driven scientific approach to business suitable to current and future market trends and situations.
The automation and smart applications will need synchronization of systems to individual AI and ML systems to communicate with and learn from each other. However, this is just the beginning, the future promises unique emerging techniques to analyze data and get meaningful real-time insights.
One key transformation that AI and ML systems is expected to usher in is a quantum improvement in how businesses serve customers. ML systems will help personalize services, providing context and answers from the considerable amount of data collected, all in near real time.
Hyper-personalization is the key to improve services by providing customers value and understanding their needs. ML will play a significant role in enabling hyper-personalization at a massive scale in the BFSI sector. AI and ML-enabled big data analysis will deliver increasingly accurate predictions and deliver to customers services that are fine tuned to their exact requirements and needs. Expect, ML in BFSI to usher in personalized financial products such as customized loans, and insurance with tailored coverage, a segment-of-one based risk assessment and pricing, and information being provided and queries being resolved instantaneously and eventually.
