Financial services companies deal with massive amounts of data today. While data analytics is a key area of investment for most players in the segment, there are many challenges that still remain unaddressed in taking these analytics projects to the next level and in achieving better outcomes.
While most of them understand the use cases for data-based customer insights and have made significant analytics investments both in terms of skills and technologies, only a few can even enumerate major gains at the enterprise level.
“The common challenges include underutilizing their analytics capabilities since they are unable to scale up or industrialize their analytics investments after they’ve completed pilot projects,” says Rishi Aurora, Managing Director and Lead – Financial Services, Accenture in India.
He adds that the lack of highly automated information feedback systems makes it difficult for banks to use data for contextual conversations at scale.
“The feedback gap also inhibits the enterprise in providing analytics-based support to decision makers at all levels. This lack of access to insights at the right time proves to be a hindrance for companies that aim to be agile and responsive in today’s rapidly changing business environment,” informs Aurora.
Clix Capital is one such a company that has significant focus on developing in-house data-related capabilities. The firm has successfully solved some key issues around scaling up. Efficiency is data capturing process is critical for this.
“Data is the fuel for analytical insights. So, as we scale up, we need to ensure that the data capture process is at par with our speed and ambition. It helps that we transition to digital acquisition channels more and more. In a digital environment, once approved by our customers, we try and understand their digital footprints with the help of API connections that we get from our external data providers,” says Katerina Folkman, Head of Analytics, Clix Capital.
One of the biggest investments that Clix Capital is in setting up its own Data Lake. Folkman’s team is preparing for its expansion with additional unstructured data flowing in.
“Having in-house decisioning is a critical capability. It will not only allow us to have full control and transparency of the decision, but we can work with A/B testing of the models and immediately enhance them as customers and markets are changing,” Folkman adds.
Another major issue—the lack of clean data –is one of the biggest hindrances for BFSI companies.
“One of the biggest challenges, in India and elsewhere around the world, is also around data standardization and making sure that the data you’re putting in through your analytics software or AI engine is clean. You may have the fanciest and most expensive systems, but if there are discrepancies and inconsistencies in the data that’s running through those systems, the end-product will also be inconsistent and not as useful,” says Aurora of Accenture.
With advanced analytics, machine learning and artificial intelligence, data can be used to derive actionable insights and create products and services relevant to customer’s needs. That said, a one-size-fits-all approach wouldn’t suffice.
“To emerge as data-based, insight-driven enterprises, Financial Services companies must measure what matters, embed analytics at every decision point, harness the power of new tools and develop an analytics culture,” Aurora sums up.
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