In a VUCA world, building social trust and creating a just, inclusive, and sustainable environment for financial activity is everything. Many companies already understand that and invest in technologies that help them make better use of customer data to improve or even rediscover financial products and services.
Big Changes in financial industry
The world becomes increasingly unpredictable, but global disruptions may create unique opportunities, especially in tech solutions. The use of data-driven methodologies, data science tools, and algorithms has already contributed to the growth of the whole financial sector. Mobile banking, virtual assistants, saving tools, personalised offers and communication – that is just a little part of how Big Data revolutionises the financial industry’s approach toward customers.
What is the definition of Big Data?
What are the Benefits of Using Big Data in Finance?
First and foremost, Big Data and the whole area of Big Data Science in the finance industry helps discover customer activity and behaviour patterns from the gathered customer data. Detailed analysis of both: internal and external data leads to stronger and more detailed strategies, better decisions and investments.
Big Data with advanced banking analytics helps:
- Simplify and automate processes
- Reduce costs of processing data
- Support operational and process transformation
- Optimise omnichannel inventory management
- Create a better customer experience and journey
- Provide must-have knowledge for better decisions
- Put customers in specific segments
- Evaluate the risks of investments in detail
- Keep track of competition’s activity
The top reasons why Big Data is globally used in banking
There are numerous areas where Big Data can be used to leverage financial businesses. Specific technologies broadly used for Data Analytics in banking are:
- Predictive analytics – helps observe different factors like customers’ activity, industry details, global changes and company’s condition to forecast trends, predict future data and the probability of occurrence.
- Machine learning – vital for data analytics, involving self-learning algorithms, Machine Learning uses the already existing data to analyse it for the prediction of possible results. Algorithms grow with the volume of gathered data.
- Data Mining – extremely essential to deal with large amounts of data, helps identify patterns and quickly discover relationships between different pieces of information and make proper decisions based on data.
- Optimisation – is crucial in banking data analytics, thanks to linear and non-linear approach, the optimisation techniques help keep different risks under and increase ROI.
- Data Visualisation – visual analytic and dashboarding tools like MS Excel, Tableau, QlikView, and SAS provide insights for banking experts and help discover game-changing details that are easier to see in visual form.
The application of the above technologies leads to solutions that are widely discussed as revolutionary for the finance market. These are: smart accounts, risk management and fraud detection, chatbots, virtual assistants powered by AI, more efficient consumer analysis, marketing and sales tools, and last but not least, improved, more personalised offers.
How to Implement a Successful Big Data Strategy for Your Business?
Managing vast amounts of data needs a proper perspective, a detailed plan and a scientific approach provided by experienced Data Scientists and Analysts. Not every bank or financial institution has such professionals in-house. Besides, the cost of having a team of Big Data experts can be unbearable for some organisations.
They can design processes that visualise data processing, design and create a solution from scratch based on existing mathematical models and use components to connect them. What is more, they can work with clients’ domain specialists to support them with tech solutions tailored to their specific needs.
The Future of Banking is Powered by AI and Machine Learning
The use of AI and Machine Learning provides unprecedented opportunities to analyse huge numbers of data and get deeper insights into customer and market behaviour.
The future of banking includes the use of AI & ML in a number of areas, e.g.:
- Client service and offer – chatbots and virtual assistants, analysis of customer sentiment, personalised banking and other ML-driven processes.
- Back office – AI-driven automation of processes, integrated command and control systems.
- Trading and management – managing wealth and portfolio, algorithmic trading.
- Security – stronger cybersecurity, AI-powered fraud detection, anti money laundering systems.
Deloitte, in its 2021 report “A higher bottom line. The future of financial services”, shows how disruptive times will create opportunities for technology in the banking sector that needs to put customers in the centre of attention:
Successful cases of using hi-end technologies show that the financial services sector is ready for brand new opportunities to take. The future of banking is already here and it’s powered by Big Data, AI and Machine Learning.
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