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Modernising banking analytics and financial services in Africa

By , Africa Sales & Channel Manager, Qlik – iOCO
Africa , 13 Aug 2021
Suhail Rassool, Africa Sales & Channel Manager, Qlik – iOCO.
Suhail Rassool, Africa Sales & Channel Manager, Qlik – iOCO.

According to global research guru, Gartner, core banking modernisation programmes are complex, intimidating, and fraught with risk, but necessary to enable digital business. This latter is, of course, no longer a nice to have but rather a business imperative in an increasingly connected world with consumers even in emerging economies reflecting a rapid rate of connection to high speeds networks.

Certainly, banking, and financial services today are no longer about the safe storage and transfer of money but rather about the flow of data; understanding risk; opportunity, and the changing needs of clients through the effective use of data.

With increased uptake of technology, the recognition and utilisation of data assets presents an immense opportunity to increase market share. In the past, expanding revenue streams could take decades but today it can be achieved in months with the big winners being those banks and financial services enterprises who have created the data streams, analytical platforms and skills required, that aim to fully exploit prospects.

Empowered Financial Consumers

Business units (BU’s) within banks have long understood the fact that today’s tech savvy consumers are connected, empowered & more informed than ever before, thus complicating the challenge of ‘lifetime customer profitability’ plus complex profit and risk models.

Data and Fintech Driven Transformation

Banks are transforming due to the disruption caused by data usage and Fintech companies. Long term sustainable growth cannot solely be sales or product focused; banking BU’s are well aligned with this reality and are dealing with the challenges associated with it. They largely have the detailed and disparate data necessary but the technology to properly combine, aggregate and analyse this information has lagged.

The technology development ecosystem within the financial services sector is driving the response to digital disruption. From modernising core platforms to managing externalisation utilising best practices in enterprise architecture and design, the banking industry is on the cutting edge of digital transformation.

Risk and regulation

Governed, accurate and timely data is vital for banks regulatory reporting. Much like ATMs permit banks to retrain tellers to sales and advisory roles, streamlining and automating data and information delivery will flip the 80/20 rule (from 80% of time spent consolidating plus aggregating data versus 20% analysing it, to 80% of time being utilised analysing and making decisions).

Three big shifts are necessary to get an organisation to achieve a status of being a data driven entity, these are democratisation of data, augmented intelligence, and embedding analytics everywhere. All these matters are challenges that often become failure points.

Top Banking industry challenges:

• Banking customer intelligence.

• Re-imagined processes.

• New business opportunities.

• Balancing risk and reward.

Making all available data accessible to workforces while providing all with the power to explore it – empowers organisation with a greater ability than ever to anticipate and meet customer needs, identify, and capitalise on new opportunities, and transform how things are done.

Widening the lens

It is necessary to prevent tunnel vision – this can be achieved by adding peripheral vision in order to acquire a holistic view. In the data world this is called Associative Analytics. Think about the traditional SQL query-based visualisation tool, and its approach. In that model a query is constructed that returns a ‘positive’ match to said query, bringing back only the data that matches that specific query, producing a very narrow result. Contrast that associative technology which not only returns the ‘positive’ match to the selection state, but also adds layers of peripheral vision. An example of this is looking at all segments who have used a savings or investment account in the past 90 Days. A traditional SQL based solution will bring back the answer to that perfectly formed question in sub second response. The real value, however, and often unexpected insight may be in the data that is excluded i.e., segments that did not use it or even customers who have not been included in the segmentation.

Augmented Analytics

It is crucial to bring powerful artificial intelligence (AI) capabilities to all use cases for analytics. Whether its conversational analytics embedded in collaboration tools; freeform data exploration inside of an application or drilling down in a pre-configured dashboard - AI should not be considered as a separate, bolt-on feature or product. It is key to deliver the means whereby all levels within an organisation are empowered with the ability to find more insights, faster and in any situation required by the business.

Embedded Analytics

Analytics need to be embedded where most needed and that is directly into the heart of the decision-making processes and applications, so that actions are driven by insights. Embedding analytics directly into critical business processes in this way, puts the power of data into the hands of decision-makers at a point in time, when and where it matters most. This, of course, leads to agile, more confident decision making. This approach also provides a unique way for external parties including suppliers, partners and even customers to expand the reach of their data and analytics.

In my next article I will expand on the methodologies that need to be applied if the financial services sector is to achieve true digital transformation and continue to drive its position as a leader in this regard.

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