Is digital intelligence really the answer?
The image of (FinTech) contenders sweeping away an old guard of established entities, certainly tells a powerful disruption story. But the reality is certainly different.
Yes, new players are entering these markets, incrementally disrupting the ecosystem and making the sector ripe for digital transformation.
However we’re seeing incumbent Banking & financial services players across the globe
respond thoughtfully, to invest in skills; notably increasing their technology investments in strategic areas such as predictive analytics, big data, cloud computing, devOps and mobile.
Is up-skilling enough in an industry where a customer focus prevails? Take banking as an example; customers across the spectrum of retail, corporate and wealth banking, are demanding more contextual integrations and expect an end product akin to the distributed commerce models of Amazon, Facebook and Uber. But what do all these changes to one-click purchase models actually mean for financial services? Is digital intelligence really the answer?
To understand this, it helps to take the view to “digitise” is a catch all. It is more about turning to key areas where the impact of digital and its capabilities can support a change, or transformation. Three strong strategic values or principles emerge:
- Customer-centricity in a manner that is highly interactive and has intelligent applications as customer’s interact with their finances, across multiple channels.
- Data focused, using data insights to provide services and products that can detect customer preferences on the fly, match them with existing history and provide value added services.
- Technology led, with experience to test, refine and develop digital capabilities.
Financial Services of the Future increasingly resembles a technology company. Data presents itself as a commonality across these three values. The industry produces the most data of any industry, every day banks alone generate a wealth of data from customer transactions, commercial banking transfers, payments, wire transfers and demographic information.
Driving change through legacy thinking and infrastructures, both from a data product as well as from a risk & compliance standpoint, presents an initial challenge. Companies need to play defence with a myriad of regulatory and compliance legislation across legacy areas of their business such as risk data aggregation and measurement and financial compliance and fraud detection.
They then need to balance this with the distinct need to improve customer satisfaction and retention, by implementing predictive analytics capabilities and generating better insights across the customer journey, thus driving a truly immersive digital experience akin to distributed commerce. Data is key to driving this change and it is possible to explore how data can be exploited, or monetised.
Monetising data sources for actionable intelligence
Business leaders are struggling to keep pace with a massive glut of data from digitisation. A data lake, which combines data assets, technology and analytics to create enterprise value at a massive scale, can help businesses gain control over their data. The emergence of cloud platforms is helping in this regard and two areas are where the predictive capability of a Big Data platform can be used to create a user experience that rivals Facebook, Amazon or Google. Two good examples are in capital markets and wealth management:
Capital Markets: Firms must create new business models and offer client relationships based on their data assets. Those that monetise their data assets will enjoy superior returns and raise the bar for the rest of the industry. It is critical for capital market firms to better understand their clients (be they institutional or otherwise) so they can be marketed to as a single entity across different channels—with cross selling in a competitive landscape.
Wealth Managers: Private banking for high net worth individuals is a digital high growth area. It is the segment ripest for disruption due to a clear shift in client preferences and expectations for their financial future. Actionable intelligence gathered from real-time transactions and historical data is critical component for product personalisation.
Modern data applications for the sector’s growing numbers of data scientists may be built internally or purchased “off the shelf” from third parties. These new applications are powerful and fast enough to detect previously invisible patterns in massive volumes of real-time data. They also enable companies to proactively identify risks with models based on petabytes of historical data.
These data science apps comb through the “haystacks” of data to identify subtle “needles” of fraud or risk not easy to find with manual inspection. These applications can help financial firms incorporate data into every decision they make. They can automate data mining and predictive modelling for daily use, weaving advanced statistical analysis, machine learning, and artificial intelligence into digital day-to-day operations.
A strategic approach to data analytics in any financial services organisation offers, massive value and competitive differentiation as it aims to reach end-to-end digitisation:
- Exponentially improve existing business processes. This includes risk data aggregation and measurement, financial compliance, fraud detection
- Help create new business models and go to market strategies – by monetising multiple data sources – both internal and external
- Vastly improve customer satisfaction by generating better insights across the customer journey
- Increase security while expanding access to relevant data throughout the enterprise to knowledge workers.
If you really think about it, the ability to manipulate and deal in data is key to the ecosystem of financial services. That in itself presents the clear partner to achieve digitisation and it is exciting to see so many adapt around the fast moving data economy.