About 87% of all companies think that big data will usher in significant changes to them by the end of this decade. Over 90% of companies think that not having an effective big data strategy in place will cause these companies to fall behind. Most businesses think big data is their key to success in this highly competitive market.
According to Forbes, big data has left the domain of cloud computing and monitoring data management companies. Big data is now a part of all of our lives. From health care to insurance, it is influencing all sectors that can use any form of prediction. Using secure data mining technologies will help these companies reduce their operational costs and maximize their profits.
Big data is now a common name in bank administrations. Every day, banks are preferring employees with knowledge of data management and data mining. If you have Agile Training, your chances of getting a data management job at a bank or insurance company automatically increase.
Big data and Internet of Things: governing financial services
IoT is how the data lake acquires all the usable data. This does raise many questions about data privacy and data sharing. Most organizations that collect data from users want to know their customers and clients better. Financial institutions, including banks, are big on big data this year. Receiving user-generated data, abiding by privacy regulations and generating desirable end-results for the admins make up a complex operating ecosystem for most financial institutions.
Big data is not about intrusion and violation of individual privacy. It is about making needs and supplies meet. Human needs are evolving each day with the need for better services and quick delivery of technology. A unified model helps most data mining companies create a data structure that is reproducible, trustworthy and reliable. This will help banks, credit unions and loan companies keep a tab on all their clients. They will have a composite view of their case histories. If there are chances of fraud, the recurring pattern of transactions and user interactions will raise red flags in the administrative systems. Right now, we are looking at Financial Industry Business Ontology or FIBO-like open standards to help industries achieve this.
Big data is not infallible; it needs better management
Mix up of names, client data and transaction details is not that old. They began with the dawn of automation. Right now, although we keep talking about AI and machine learning, the user-end is not as sophisticated as the data management software and frameworks available. Most business data analysis happens in real-time.
How are financial services benefiting from big data analytics?
Big data has found an irreplaceable role in financial services.
Several banks and financial institutions use analytics to diagnose and terminate fraudulent interactions. Legitimate business transactions have some telltale signs. A detailed analysis of customer's transaction history along with a comparison with other cases of fraud gives these financial services a clear view of any behavior that is a sign of fraud. Adapted analytics systems can suggest prompt action that can minimize the damage and stop fraud before it even occurs.
Most financial services stand on the heavy regulatory framework. This requires rigorous monitoring and reporting. The 2008 Dodd-Frank Act, requires every deal to be carefully monitored and finely documented. Each trade requires detailed credentials as well. This came after the American Financial Crisis of the 2000s. Trade surveillance uses the data. It helps in the identification of abnormal trading patterns.
Customer segmentation is a must for any financial business to run securely. Financial services have seen a recent shift from product-centric to a customer-centric conduct. Segmentation helps companies to understand their customers better. This is especially necessary for all businesses that involve direct customer interaction like loan corporations and banking services.
Personalizing marketing comes after segment-based marketing. This helps businesses understand their customer’s buying habits. This will further help these companies segregate the customers by their buying patterns. This is required for merchant records, financial services firms and insurance companies too.
This can involve pooling data from customer profiles across several platforms. This may include the information customers have submitted to the company representatives and servers and the data from social media profiles. This raises several eyebrows and exacerbates the concerns about violation of online privacy. It also helps financial business firms make decisions that are more informed. For example – companies can better decide if they want to consider Person A for a big loan or if Person B owns a summer house that needs immediate repair.
Management of risk
Every business needs some amount of risk management. Incidentally, financial services are one of the key industries, and it requires more than the average risk management services. Base II is a regulatory scheme that requires their client firms to manage liquidity through testing of stress.
Similarly, financial firms also manage customer profiles and customer risks by analysis of customer portfolios. Algorithmic trading is not foolproof. Most companies engage in back testing strategies on historical data to make sure their customer analysis algorithm does not backfire. Big data in financial services also supports real-time risk assessment of any transaction, client interaction, and customer assessment.
Over 25% of the financial service firms in the USA already use big data projects. They have a distinct competitive edge over those who have not implemented similar projects. The data lakes contain all kinds of verifiable information of business trades, individual transactions, and customer data. Big data might be a solution; it is not one without significant expense. Due to the combined requirement and perceived value of such big data projects, most financial firms will make use of big data. They will hire more staff with skills in big data management. Data engineers and data scientists will soon become an everyday job requirement for banks, loan agencies, credit unions and even insurance companies.