Data Management is the term used for the methodologies and technologies for collecting, sorting, processing and analysing huge and complex data sets. Its business goal in the financial services industry is to obtain real-time insight from the data in order to drive your business forward. There are various data-driven analysis techniques that can be utilised on these datasets to boost business growth. These include, for example:
Expanding data footprints, new regulations and AI solutions are just some of the trends that will impact data analytics in financial services over the coming years. The sector is predicted to contend with innovation demands, together with regulatory compliance.
The following five trends are affecting data analytics in financial services.
In the past, large financial services organisations with various different departments would have separate Data analytics platforms, for example, each of the following:
As a result, data mining and data exchange amongst the various departments of the business were extensive and time consuming.
The rise of mobile banking and the demand for instant banking experiences has significantly fuelled the digital transformation of the financial services industry. With this, has come enormous data growth with each transaction, mention, comment and interactions on mobile applications, web pages and social channels stored and analysed.
Unified data analytics platforms are making it possible for financial organisations to use a data analysis system which is efficient and effective, with customised data science environments. Data scientists will be able to use personalised and data-driven work conditions with a basic user interface which can build and deploy machine learning with ease throughout the organisation. Platforms will make data management more straight forward and increase the quality of data.
In the future, we will see next-generation, cloud-ready and unified Big Data analytics platforms based on open-source stacks. The most crucial thing for financial services organisations to do is to securely gather and store the significantly increasing volumes of data, whilst successfully and compliantly obtaining value from big data with advanced data analytics.
Highly personalised recommendations and offers can be provided to banking and insurance customers through the effective use of Big Data. When utilising the correct tools, financial organisations can analyse and manage huge datasets with the ability to predict their wants and needs at micro level.
Remarkable banking experiences could be achieved by banking and insurance organisations, which are tailored according to individual customers. The ability to achieve this begins with effective data management and data analytics at scale to deliver a customer-centric approach.
Inclusive finance is a growing trend towards financial products and services that are:
Banks and insurance companies can utilise consumer data analytics to identify the barriers to financial Inclusion. Such insights can give a better understanding of customers and, as a result, enhanced solutions for inclusive banking and insurance experiences.
Robo-advisors can provide low-cost and real-time financial portfolio advice to customers which is personalised. Simple versions of robo-advisors are chatbots, which can:
It is predicted that by 2025, robo-advisors will offer automated financial planning services based on algorithms, without human intervention. Big Data analytics will be used to achieve this. They will collate data about a customer’s financial circumstances and objectives through online surveys and can use this to automatically provide financial advice and to invest in assets.
Financial organisations must adhere to strict regulatory requirements, such as those which govern access to critical data and accelerated reporting requirements.
Government regulations, both changing and emerging will continue to have an impact on data management and data analytics in financial services. In order to stay on top of changing rules and regulations and to avoid risk, financial organisations need robust, yet agile data management.
Customers are also becoming increasingly aware of and concerned about how their information is managed by financial organisations. To gain the trust and loyalty of customers, transparency is key.
For financial organisations, Artificial Intelligence (AI) provides the ability to analyse an enormous amount of data from an array of sources in order to:
Mortgage applications could go further than traditional data analysis methods, with the addition of social media data. Big Data could be utilised during the application stage, pulling in relevant information from bank records, websites and public databases in order to build a more concise picture of the applicant.
Machine learning algorithms can also be used to score mortgage applications, saving time, money and allowing mortgage lenders to reach more customers.
Financial services organisations are facing a vast array of new data, increased competition from new start-ups and neo-banks and increased customer expectations to be central to everything they do. The key to success is ensuring that data is in good condition, supports effective, timely decisions and business objectives. Catalyst BI’s Enterprise Data Management Health Check ensures that data is fit for purpose, as well as technologies, processes and governance.
Combining both technologies and behaviours to measure and manage data and processes, it enables better discovery, transparency and value, scoring a financial organisation’s data management on the following key pillars:
Financial organisations can utilise the EDM Health Check to ensure that they are leveraging the best technologies available.