Augmented analytics is proving to be a gamechanger across industries—its impact is most apparent in streamlining data management processes, making data accessible to business users, and improving products/services offered. According to recent research, the augmented analytics market is set to expand at a CAGR of 30.6%, growing from USD 4.8 billion in 2018 to USD 18.4 billion by 2023, signaling the increasing importance of this approach across verticals.
In the context of banking and financial services, the industry has witnessed the proliferation of data—financial records and transactions, customer purchase histories, third-party sources, mobile apps, text messages, social media channels, and so on—on all fronts. However, legacy BI systems continue to be ill-equipped to deal with large influxes of data, making intelligent data processing and automated methods the need of the hour.
Further, with recent crises and shocks impacting financial institutions’ health, credibility, and decision-making capabilities, poor data quality becomes an immediate problem statement to address. The challenge of data duplication, with entries recorded multiple times and available over different platforms, also makes standardization and data quality elusive. This in turn has far-reaching negative consequences for regulatory compliance, risk management, money laundering and fraud prevention, and adherence to international regulations, among others.
Ensuring the integrity of data therefore becomes a crucial foundation for digitalization in the BFSI industry. This is where augmented analytics contributes to several key areas, such as the following:
Data from internal and external stakeholders can be sourced and made available on a single platform to create an organized inventory of data assets enriched with metadata. Inconsistencies, errors, and redundancies can be reduced here through intelligent, automated tools. Further, a standard set of data management processes, with AI ensuring secure entry, storage, and access, can ensure better data discovery, regulatory compliance, data security, and reporting processes.
With data becoming increasingly complex, manual collection, cleaning, labelling, and analysis remains a time-consuming endeavor. The manual nature of this work also makes data prone to bias, human error, and inaccuracies. Here, AI and ML-based data preparation processes can save time and costs while increasingly accuracy significantly. It is estimated that augmented analysis reduces the time taken to gather, prepare, and curate data by approximately 50-80%. For example, when augmented analytics was applied to data collection for a banking business, data was harmonized from internal sources—from over 40 countries—such as transactions, ATMs, deposits, and credit cards as well as external sources such as money transfers and PEP data. This resulted in a reduced preparation time of one day—a staggering 95% reduction compared to previous processes.[3: Augmented Analytics is the Future of Analytics]
As various functions within a single financial enterprise, such as risk, operations, compliance, trading, and auditing, can interpret and utilize the same data differently, augmented analytics helps generate contextual insights that cater to specific needs. It also enables a move from mere data consumption to intelligent insight generation, alongside aiding storytelling through data.
Moreover, this widespread availability of data encourages the use of analytics in everyday processes, leading to greater collaboration across functions as well as data literacy. In the context of the latter, the number of citizen data scientists is set to grow five times more than professional data scientists[4: Augmented Analytics is the Future of Analytics]—indicating the possibilities of greater analytical independence for businesses and data accessibility in the BFSI industry.
According to a report, by 2021, augmented analytics will drive new BI and analytics purchases, data science and ML platforms, and embedded analytics. Further, 50% of analytical queries will either be automatically generated, or generated via search/NLP.[5: Augmented Analytics is the Future of Analytics]
With the benefits of augmented analytics being manifold, let’s look at how it applies to specific use cases in the BFSI industry:
Extending credit to individuals and businesses requires informed decision-making, which in turn requires comprehensive, high-quality data. For instance, identifying opportunities to cross-sell credit cards would require AI-driven customer classification, as well as extensive information on factors such as monthly expenditure and products purchased.
Augmented analytics can bring together data from external sources, previous transactions, and behavioral datasets to facilitate decisions on creditworthiness and credit risk, with AI also helping develop customized credit risk applications. ML processes can further reduce the time taken to obtain these insights by integrating millions of parameters and instantly highlighting patterns in data. The end-to-end automation of analytics can therefore improve decision-making, reinforce recommendations with solid evidence, and make business information readily available to users.
This explainability can decrease the risks associated with defaults, improve the profitability of loan portfolios, bolster outreach to niche customer segments, and overturn low public trust in financial services—inspiring confidence in data-driven insights. For instance, thin-file credit scores, which can take weeks to determine, can be built in a mere hour by microsegmenting customers and testing multiple models in a short time span.[8: Augmented Analytics is the Future of Analytics]
One of the most immediate advantages of analytics in the BFSI industry is helping businesses examine and capture new opportunities in the market. Augmented analytics can not only help identify causes for existing problems and uncover previously unknown patterns of growth but can also provide concrete, actionable insights to business users on viable investments, growth, and avenues for success. For instance, it can identify previously untapped customer segments as well as drivers affecting insurance purchases, which can in turn impact selling strategies.
With financial institutions having to deal with increasingly complex regulations, rules, penalties, and government mandates, augmented analytics can quickly integrate new information into existing processes. For example, it can help review 800 million pages of regulation material per second, subsequently offering decision-making support on regulatory reporting requirements, consumer protection, and so on.
While chatbots and voice assistants are being employed by BFSI players to provide more personalized services to customers, such technology can be supplemented through augmented analytics to make data available to business users as well. Natural language interfaces are now replacing traditional BI dashboards—applications such as voice and text search, chatbots, and personal assistants are simplifying access to data, translating output in terms of natural language as well.
While augmented analytics is still an emerging technology, it is clear that it is set to revolutionize the analytics landscape, making data more immediately available to business users and reducing their dependence on traditional analytics processes and manual efforts. This technology is poised to bolster automation for businesses, marking the next step in the evolution of the BFSI industry.