eBusiness Weekly
Many economists and Information Scientists are claiming that Big Data is now the new oil for the financial services. Global management consulting firm, McKinsey, has referred to Big Data as “the next frontier for innovation, competition and productivity.”
Many financial institutions are fast migrating to the usage of Big Data in making more effective decisions, intelligently tapping into the fast growing stream of social media feeds, online transactions, video and other unstructured data sources. “Big data” is the field of computing science that seeks ways to analyse, and systematically extract information from, or otherwise deal with very large and complex data sets which cannot be easily analysed using traditional data-processing applications.
Critical to the success of using big data analytics, requires a clear understanding of the three defining properties of Big Data, which are volume, variety and velocity. These are also known as the 3Vs.
The value of Big Data to the financial services industry is immense. Immediate cost-reduction opportunities lie in fraud and sanctions management, while account management can be enhanced by enhanced customer insight.
Taking a longer-term view offers Banks the potential of significant new revenue streams.
In the Big Data world, spotting the relatively small number of fraudulent transactions in a sea of legitimate payments becomes less difficult, despite the sizeable shift in behavioural patterns towards electronic and mobile/internet payments.
Banks have unique insight into how, where, with whom and when customers are spending money — by analysing such information, banks can build an insight into customer intelligence and behaviours that they may well be able to monetize.
Checking customers’ names against a sanctions blacklist can become highly complicated in a world where a Bank has multiple customers with the same or a similar name.
By using Big Data techniques, this reputational risk can be mitigated and managed. Big Data can also be used to enhance account and relationship management.
By co-coordinating the collection of data already in the public domain — such as share price movements, a change in auditors or a director selling shares in his company — and passing it to account teams, understanding of key client businesses can be improved.
Further insights can be derived from additional internal data, perhaps focused around the early identification of potential problems — for example, how credit lines are being used against agreed limits; to monitor account crediting behaviours, as problem accounts often credit funds late in the day; and to identify payments patterns of potential interest.
Another exciting area for Big Data lies in the potential to create new income streams for banks. Also banks can track consumer sentiment, test new products, navigate the marketplace, manage business relationships and build customer loyalty in new and more powerful ways.
However, Big Data comes with many challenges, relating mainly to its complexity. Some of the basic show-stoppers relate to how banks can understand and use Big Data streams which come in an unstructured format, such as text or video or how they can capture the most important data as it happens and deliver the data to the right people in real-time.
The other Big Data challenge relates to how banks will be able to store, analyse and retrieve Big Data given its size compared to the limited computational capacity of most institutions’ current systems. Added to these complexities are numerous other challenges, ranging from legal issues and concerns around data privacy, security of access and deployment of systems.
The changing financial landscape today
As we delve in to the impact of big data, let us learn a bit more about the role of modern technology in the financial services sector.
At the global scale, commercial retail banks are having to contend with ongoing risk, compliance and lack of perspective into their breadth of products and clients.
Meanwhile, specialised banks or niche financial providers, continue to gain market share across both consumer and commercial banking services such as lending, treasury services, card business as well as other value added offerings.
Whilst traditional retail banks with brick and mortar branches, very high head-counts and legacy computer systems continue to offer valuable services to consumer and business clients, they are, however, struggling to compete with emerging online-only banks, which are offering many of the same services as their fixed-location competitors, but at much lower fee structures. These modern banks are now enjoying higher use cases, transaction volumes as well as higher savings deposit rates.
The Big Data opportunities for Zimbabwean financial services players
To meet the challenges facing them, the financial services players need a new set of unified data platforms that converge all data into an interwoven data fabric that will allow them to store, manage, apply and analyse data at the right speed, at sufficiently large scale, and with a great deal of reliability.
Today banks now know this, and in response they are placing increasingly greater emphasis on building converged platforms that are capable of running all manner of intelligent applications.
Harnessing artificial intelligence, enabling the learning of patterns and then deriving insights independently, these intelligent applications vastly increase the time-to-market of new products.
They also support current financial modelling and analysis programs and other products currently existing in the market.
It is an open secret that for example, fewer and fewer financial institutions are now relying on the traditional methods of gleaning information about their customers from face-to-face interactions, but these new platforms are taking over and helping banks in gaining a 360-degree view of their customers by integrating sentiment data with customer data as well broader market trend data, using sophisticated algorithms.
Emergent data management platforms are capable of, not only capturing and storing enormous volumes of structured and unstructured data, but also creating a global data tapestry that enables both simultaneous and distributed analytics and applications.
This has enhanced the ability of banks and other financial services players to integrate new applications into their operations, and make incremental improvements to the customer experience in real time.
As financial institutions become more mature in data science and begin to push these analytical boundaries, machine learning (artificial intelligence) and the predictive analytics will become the new frontier for success. It is in this regard that our local Zimbabwean banks must begin to give this new science, a great deal of attention.
In the Zimbabwean financial services space, there is a growing focus on automated applications. However there is still very limited use of big data to drives application without the need for human intervention.
In the coming years, investment focus will primarily be on cybersecurity and other security initiatives as well as platforms to monitor customer activity. However, the big opportunity lies ahead, with emerging data platforms, the power of big data analytics and machine learning can be harnessed and leveraged to illuminate new pathways in business-critical areas of operations, including client and market optimization, holistic product development and risk management as well as regulation and compliance management. The Future is therefore very Bright!!!
◆ The writer is an economist. The views expressed in this article are his personal opinions and should in no way be interpreted to represent the views of any organisations that the writer is associated with.
