To cope with the disruption in their business models, banks need to manage high data volumes, including managing unstructured data, in real time. To achieve this, banks are extending their existing data warehouse with NoSQL data, and offloading computation and storage to public or private or hybrid clouds. Banks are replacing and augmenting their Extract, Transform and Load (ETL) with intelligent data ingestion and data preparation, augmented by automated data governance enabled by semantic definitions. Banks are also building linguistic computing capabilities to manage unstructured data.
This blog explores some emerging technologies being embraced by banks with respect to building intelligence, managing large data volumes, managing cost and building flexible and configurable technology.
To make engagement with digital natives intelligent, banks in today’s world are collecting and analyzing GPS, spatial and cyber logs on multiple tracks. They have also been seen to implement data lakes to manage the number crunching required to handle such huge and low latency data volumes. Customers need real time data and banks in sync with the requirement are leveraging Internet of Things (IoT) in ATMs, mobile banking and weblogs. A way ahead of all these steps to build intelligence, Machine Learning techniques are being implemented in the risk management and fraud arena for real time analysis and alert generation. A cohesive effort of these is only enhancing the customer experience quotient, a much needed factor in today’s competitive world.
Managing large data volumes
The volume of data generated and collected in the banking sector is enormous. But in this age of big data, are those data being utilized or applied properly? With the wider use of unstructured data, the data at banks is growing significantly every year. Existing data solutions are not geared to cope up with this massive data growth. In such scenarios, banks are looking to implement data lakes embedded with intelligence to manage the enormous volume of text, voice and video. To ensure a definitive process and allow the industry to largely benefit from this huge data clutter, new transaction processing as well as analytics architecture is now embedded with data lineage, data governance, data intelligence and reconciliation capabilities in data preparation. This helps banks to address challenges created from multi-format, multi-source, multi-definition, low latency data requirements. Yes, banks need to build predictive capabilities to manage the large data volumes, and that is why banks are investing in data science to generate better returns, propel customer engagement and adhere to compliance guidelines. The industry is on a roll.
The consistent problem with banks is to manage costs effectively. Surviving competition and rendering optimum customer value at the same time is a key challenge that banks face. In such a scenario, to garner savings and optimize costs, banks are leveraging data lakes on commodity hardware/appliance with open-source software. They are also streamlining the decision on data management by helping existing data warehouse to offload storage and computation to cloud. In a separate attempt, within cloud, they are dividing the load between archival storage vs daily use data as well.
Building flexible and configurable technology
Flexible data integration and data preparation capabilities leveraging big data and data lakes are helping banks to create visible and intelligent transformation. This helps in creating the traceability needed for privacy regulations like CCPA and GDPR and BCBS 239 and can also help in replacing monolithic ETLs with data streaming and API integration capabilities to support Open APIs and marketplace. The regulatory hunger for data has grown 10X in the past 10 years or so and this is clearly likely to grow by another 10X in upcoming five years. Therefore, banks are building microservices-based risk, finance and regulatory infrastructure to address the upcoming changes. They are also embedding intelligence and flexibility into their data infrastructure to make the overall operational processes seamless and convenient for customers. At the end of the day, customer is the king.
The use of emerging technologies by banks is enabling the twin objectives of configurable intelligent technology to handle large business volume in a very cost efficient way- a must to support disruption in existing business models.
Wipro Fellow, Distinguished Member of Technical Staff
Mohan has more than 20 years of industry experience across risk and compliance domain, including defining policies, processes, TOMs, and technology systems. A Digital and Cloud Strategy and Business Model Expert, Mohan has also authored quite a few internationally published books. He is a Fellow at Distinguished Member of Technical Staff (DMTS), Wipro. He has been an Invited expert at Gyan Sangam – Conference of 150 CEOs of Banks and Financial Institutions and top management of RBI – By Ministry of Finance Government of India – 2015 and 2016. His fourth book Bank 4.0: Future of Banking Technology is under publication by Springer