Introduction: Augmented Analytics in Banking, and the Fourth Industrial Revolution
The next big challenge in the banking industry is this: enabling every business decision-maker to harness the superpowers of Augmented Analytics using AI, whether they are trained in data science or not. Augmented Analytics marks the next wave of disruption in data science that uses AI to automate data preparation, exploration, analysis, and visualization in Business Intelligence(BI) platforms. The global Augmented Analytics market was valued at USD 7.7 Billion in 2020 and is projected to reach USD 62.5 Billion by 2028.
The Fourth Industrial Revolution is being ushered in through the advent of augmented human intelligence that is essentially a seamless confluence of the physical, digital and biological worlds. The “end of code,” as we know it, is near, as we are collectively moving along the continuum of smarter, more integrated, and more evolved problem-solving.
Making Augmented Analytics capabilities percolate to the last mile of banking can make or break data-driven decision-making. It is now more critical than ever that professionals across the banking sector who are driving business outcomes at different levels can predict their next-best move using accurate, granular-level, and near real-time insights using AI-powered Augmented Analytics, without having to run to a data science expert every time.
Why is Augmented Analytics a Must-Have for Bankers Across the World?
Banks are constantly struggling to improve the effectiveness and efficiency of their offerings while bringing down the cost-to-acquire, cost-to-serve, and cost-of-access. In particular, the increasing trust deficit among customers is a major concern. According to a study by Accenture, only 29% of respondents trust their banks to look after their long-term financial wellbeing, compared with 43% two years ago. This increasing lack of trust has immense implications for banking businesses, like a sharp rise in the customer churn rate, drastically reduced customer lifetime values, and a sharp rise in the cost of customer acquisition.
Omni-channel digital engagement by banking customers has been accelerated by leaps and bounds due to the COVID-19 pandemic. For example, according to McKinsey, in the first few months after the onset of the pandemic, the use of online and mobile banking channels rose by an estimated 20%-50%. Further, between 15%-45% of consumers are expected to reduce physical visits to bank branches post-crisis.
The increased use of online interfaces in banking means that customer expectations are shifting rapidly, and keeping up with the low attention span of customers on digital channels is now a harsh reality that bankers need to tackle.
Further, the inability to reduce Non-Performing Assets (NPAs) is draining banking institutions across the world. For example, a decrease of just 1% in the default rate can improve the profitability of a loan portfolio while increasing the reach of financial products to long-tail financial consumers. These indicators signal the emergence of a radical transformation in the banking industry and underscore the need for banks to fundamentally rethink how they can bring excellence in providing customer experience at the center of everything they do.
Augmented Analytics in Action - with MECBot
Powered by innovations in AI, MECBot is a leading data excellence platform that solves the key enterprise data challenges in an end-to-end manner and enables insight-driven decision-making without relying on the underlying databases or the structure of the data. It is the go-to data analytics platform for several leading Fortune 1000 clients across the globe in Banking, Insurance, Retail, Sports, Healthcare, and more.
MECBot puts the business first by adopting the Entity Domain Model approach. It comes bundled with a self-service, intuitive interface and takes care of the key data management and analytics requirements in a trusted and centralized manner, including scalable deployment.
How MECBot Works
Simply put, MECBot automates the entire data unification at scale and creates Smart Data Folds to deliver unprecedented business results. To accomplish this, MECBot first structures the unstructured data contextually using domain-specific business ontologies and marries it with structured transactional data in near real-time.
MECBot works in a three-pronged manner to give its users the best possible data experience. The three action pillars of MECBot are - Manage, Explore, and Comply.
Manage: Effortlessly manage your entire data ecosystem.
Unify all your data, analytics, models, and insights in a single all-in-one platform, built on top of robust, scalable, and reliable technologies. Say goodbye to data silos by unifying, integrating, and synthesizing data across multiple formats, sources, and teams. MECBot creates Smart Data Folds and generates a trusted enterprise data fabric across all the data sources.
Explore: Explore all your data swiftly and at scale.
Visualize your datasets and unearth hidden patterns. MECBot helps data teams to intelligently address queries as and when they arise. Insights on MECBot are available as a self-service through free form search and ad hoc querying.
Comply: Comply by providing trust and governance across the data lifecycle.
By keeping the data noise-free, tamper-proof, and consistent, MECBot ensures that you always work with trusted datasets that are reliable, verifiable, and traceable. It provides data lineage, audit trail, version changes, cataloging, and role-based access with banking grade security.
With MECBot, users can:
- Automatically run a trusted Marketing ETL (Extract, Transform, and Load) pipeline.
- Auto-clean, pre-process, and visualize all data using native reporting/external visualization tools.
- Create a flattened data view for entities without any complex SQL joins.
- Auto-detect patterns and generate insights on demand through datafolding and free flow search.
- Build models like loyalty analytics and segmentation without the need for moving the data around.
- Create custom ML models for the banking domain in a low-code environment along with intelligent recommendations for best-fit algorithms and suggestions for model performance improvement.
MECBot Turbocharges Banking Analytics with Augmented Analytics Using AI
MECBot is a next-gen Augmented Analytics product that makes banking analytics seamless, personalized, timely, actionable, and insight-driven. It:
- Automates data pipelines and workflows that can be auto-configured and re-used without any coding by the users.
- Comes loaded with self-service AI models that automatically reveal accurate answers to all decision-making queries made by the user.
- Delivers observable and explainable AI that uncovers hidden relationships in the data and generates actionable insights in seconds.
- Provides accurate and scalable predictive analytics that create intelligent forecasts and predict outcomes for all possible what-if scenarios.
- Turns the data analytics experience into a conversational mode using Natural Language Processing(NLP) and Free Form Search where users can simply ‘ask questions’ and ‘get answers’ in natural English language.
What Can Banks Do With MECBot’s AI-Powered Augmented Analytics?
Deliver Highly Personalized, Valued-Added Experiences to Customers at Scale
Banks can use MECBot’s out-of-the-box modules like customer 360o, churn rate reduction, loyalty analytics, MROI optimization to create lasting experiences for customers that are effective, consistent, and engaging, irrespective of the channel, geography, or employee providing the specific banking experience to the end-customer. (McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value to global banking each year).
Democratize Data Use Across Teams and Roles by Enabling Non-technical Users
MECBot achieves this by automating the data preparation stage and transforming the data analytics and data visualization stages into low-code. In data science, ETL stands for extract, transform, and load, which is the process of sourcing, copying, unifying, cleansing, and transforming datasets across multiple sources and data types. According to this article, it is estimated that data scientists spend more than 75% of their time on ETL, which MECBot unique Augmented Analytics capabilities can reduce steeply by automating several steps in the data preparation process.
Shrink the ‘Data to Insight’ and ‘Insight to Action’ Cycles for Reduced Time-to-Market
Despite sitting on top of a massive pool of data, bankers are struggling to make timely decisions that positively impact business top-line and bottom-line. To truly democratize Banking Analytics, banking users must be able to move from data to insight and from insight to action within a fraction of the time that they are taking now. With MECBot’s sharply reduced data lifecycle, bankers can develop new products based on market trends and customer preferences within a very short period, thereby getting a distinctive time advantage in the stiffly competitive banking environment. Using MECBot’s smart recommendation engine, bankers can identify the best-fit next-to-buy products across customer segments and design optimum cross-sell & up-sell campaigns.
Make Insights Contextual and Explainable to Improve Decision-Making Success
MECBot cleans, transforms, and joins together structured data, unstructured data, and poly-structured data from Financial Databases, Customer Demographics Databases, Banking, and Financial Literature, Research Reports, Banking Data Warehouses, etc. in real-time and at scale. Understanding the banking domain and situating the use case scenarios in the context of the targeted market segment is essential to bringing accuracy and success to the decision-making process. Tools like Smart Data Discovery that auto-detect hidden patterns in data to address the various underlying nuances in decision making are necessary to keep pace with the ever-changing dynamics in banking customers.
Reduce Compliance and Other Risks Through MECBot’s Inherent Data Governance Features
Banking regulations are uniquely challenging for marketers since the ways in which the personal data of target customers can be obtained and used are extremely limited. MECBot helps to implement a data governance strategy and reduce non-compliance risk without compromising on marketing effectiveness. Banks can also avoid other risks like the risk of underperforming products and skyrocketing NPAs by using a mix of customer demographics, credit rating, transaction patterns, social media listening & sentiment analysis to pull down default rates by design. They can safeguard product lines against credit risk, manage and streamline eKYC, combat fraud & money-laundering, and also predict and prevent exposure to foreseeable market risk.