Big data scope and scale, collaborating and analysis techniques for data-driven products – this is how banks and FinTechs can use big data to understand customer needs better
Are you using big data to build better, data-driven financial products?
More financial institutions are realising that the big-picture data of how individuals behave elsewhere and perhaps even outside of their business scope can deliver remarkable new insights that help them identify trends, patterns, and correlations to make predictions and ultimately build better products.
But a lot of the big data on your customers lies outside of your business, so to really make use of it, we delve into strategies for legally and safely attaining and then using big data in banking and FinTech, including:
Here’s what you need to know about using big data to understand your customer’s needs…
Traditional data deals with structured information in manageable amounts. Your monthly expense report, for example, is a simple spreadsheet with several expenses listed on it – you can use it to calculate totals, spot patterns etc.
But an expense sheet alone doesn’t tell you who a person really is, what they truly need or how they will act in the future in a broader sense.
That’s where big data comes in. Big data takes on the challenge of combining your internal insights (expense sheets for the year) with vast and unstructured data sources, like social media posts, images, sensor readings etc. The idea being that you can build a much clearer and more useful image of the real person using big data sources.
By harnessing the power of big data analytics to augment your customer data analytics, banks and FinTech companies can unlock unprecedented insights to help you personalise services, create tailored investment strategies, and enhance risk management. In short, it gives you more strategic data-driven product development.
Also, see how to use data to boost customer loyalty, get ideas for exciting new fin products with our look at blockchain in banking and then learn how to perform in-depth customer feedback analysis, how to frame customer interview questions for deeper insights and how to hyper-personalise and drive engagement with full customer data analytics.
The first hurdle with big data, though, is the fact that many data sources lie outside of your company, so how do you legally access them?
Collaboration can go a long way to unlocking access to broader datasets. As a bank or FinTech, you have several FinTech collaboration options, including:
For effective collaboration examples, see what a week in an agile development team looks like.
And, for better feedback, get a more in-depth look at the costs, benefits and uses of various customer feedback and experience platforms.
Possibly the most enticing partnership is collaboration between traditional banks and FinTechs, and it can take many forms.
Banks can merely supply anonymised data for FinTechs to analyse, in exchange for sharing their findings/insights. Or you could have more integrated partnerships, where the FinTech agrees to develop analytical tools and capabilities for the bank using the data.
Another model still is an agreement to co-create new products, where the bank supplies the data, market and some capital, and then the FinTech does the product development – with an eye on bringing successful products into the bank’s main stable in exchange for revenue share or a lucrative exit for the FinTech.
Regardless of the collaboration model, it needs to be compliant with financial regulations and data-sharing rules. A useful approach can be to take a privacy-by-design approach, where integrating data privacy and security measures at the outset of any data project or collaboration by either anonymising or pseudonymising the data so that no one record can be linked back to an individual.
Secure data-sharing technologies such as blockchain and distributed ledger technologies can be very useful here since they’re encrypted and tamper-proof. Otherwise, API-based data sharing such as with open banking allows you to securely share data, with customer consent at its core.
Either way, you’ll need very clear data-sharing agreements that outline the scope of data to be shared, the purposes for which it can be used, and the measures in place to protect it.
See the complete guide to scaling your software, the latest in FinTech and banking UI trends as well as why you need design thinking in finance and learn about using cloud computing for scalability and more effective FinTech collaborations.
Once you have access to big data, you need solid methodologies for analysing it so that you can extract what you need from it. Here are some advanced techniques:
Data from different sources must be integrated into a coherent dataset. Aggregation is then used to summarise data, making it easier for your internal team to analyse patterns and trends. See how to hyper-personalise and drive engagement with full customer data analytics.
Data mining involves exploring large datasets to look for clear patterns and relationships, with the aim of clustering and segmenting data into distinct categories for your team. Another approach is to use visualisation tools to express the data in charts, graphs, and heatmaps so that human analysts can actually work with it.
Possibly the most promising approach is to use machine learning algorithms to help model complex relationships within data that are not readily apparent to human eyes. Techniques like decision trees, neural networks, and deep learning can help you extract useful insights from massive data sets with ease, but also forecast customer behaviour, identify fraud patterns, and optimise financial strategies – essentially using AI to do statistical modeling. See how to use AI and machine learning for personalisation and broader toolsets for personalisation at scale.
Natural Language Processing (NLP) algorithms can analyse unstructured data like customer feedback, social media posts, and news articles to extract sentiment and thematic insights for understanding customer needs and market trends.
You can even use it to run simulations – modelling different scenarios and possible outcomes – to help with risk management and resource allocation.
Hadoop and Spark are frameworks that can process vast amounts of data across clusters of computers, making them ideal for big data analyses. Hadoop is known for its storage and processing capabilities, while Spark offers fast analytics and supports real-time processing.
There are also other cloud-based analytics platforms that can provide scalable resources for storing and analysing big data.
See how to use analytics to boost engagement.
Get a more in-depth look at the costs, benefits and uses of various customer feedback and experience platforms.
Once you have new data insights, you can use them to develop new products using Lean, Design-Thinking and Agile processes:
Use the insights gathered to brainstorm new financial products or enhancements to existing ones. Consider how technology can solve the identified needs, such as through digital wallets, personalised investment solutions, or real-time financial advice.
Learn how to check if an app idea already exists, how to frame customer interview questions for deeper insights and how it unlocks data-driven product development.
Assess the viability of these ideas through prescriptive analytics, modelling potential market impact, profitability, and risks. This step ensures that the concept aligns with business objectives and customer value. Also, take into account regulatory compliance and security.
See how to validate your app idea before investing in it and why you need design thinking in finance.
Develop a minimum viable product (MVP) or prototype and use it to gather real-time feedback from early adopters. Use A/B testing and real-time analytics to understand how the product is being used and identify areas for improvement.
See a selection of successful VS failed MVP examples, and learn to use A/B testing in digital banking.
You can use a continuous stream of analytics data and customer feedback to refine the product, focusing on features, usability, and performance. Then, once it seems to be well adopted, you can start incorporating machine learning to personalise the product experience for individual users based on their behaviour and preferences.
See the 9 things you should know before you start developing an app, the ins and outs of personalisation at scale and how to boost innovation in banking and finance.
If you seem to have a winner, develop a marketing strategy that highlights the data-driven benefits of the product, using the customer insights you have thus far to inform marketing messages and channels.
Remember to review security and regulator compliance, and then keep scalability in mind so that you’re sure the infrastructure to support growth as the product gains traction.
See the entire app development process from start to finish and learn how to save up to 80% on app development costs.
Keep monitoring the product’s performance using real-time analytics, and then be prepared to adapt the product in response to new insights, changing customer needs, or shifts in the financial landscape. Continuous innovation is key to staying relevant.
See how much creating a new app costs and why you need a digital consultant.
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