How Analytics & AI Are Transforming The Banking Industry
Updated: Sep 3
Welcome to the world of AI, data and people analytics! In the 1980s and 1990s, IT systems transformed banking. Today, banks have an opportunity to reinvent themselves again – with data and analytics. Every major decision that a bank takes to drive revenue, control costs and to mitigate risks can be infused with data and analytics. In the early days, some of the banks used ATMs to create competitive advantage for a few years. Some banks used the internet to create a differentiated online position for themselves. Over the next few years, data and analytics are expected to be the differentiators, with banks trying to catch up. Banking institutions have an opportunity now to decide where they would like to make some smart, targeted investments, if they want to make a significant impact.
Stephen Hawking, the renowned theoretical physicist once said, “It’s tempting to dismiss the notion of highly intelligent machines as mere science fiction.” Most AI systems however, are embedded in lines of code and do not have a physical form. The term “AI” includes all technology used to mimic human intelligence, typically falling into one of three subcategories: machine learning, natural language processing and cognitive computing. Digital disruption is rapidly transforming the business world, backed with insight gained out of Artificial Intelligence and Machine Learning (AI / ML), thereby enabling Data Analytics. When Amazon recommends a book you would like, Google predicts that you should leave now to get to your meeting on time, your bank stops a fraudulent transaction on your credit card, and Ola magically gets a cab of your choice at your doorsteps at the prescheduled time, these are examples of machine learning over a Big Data stream.
The financial industry is a data intensive and highly regulated industry. Due to the advent of new entrants like Fintechs, digital and payment banks, new regulations and change in customer behaviour, the traditional banking system is facing an external disruption and tension to reinvent itself and critically examine its business processes, to not only get more clients but how to enhance existing customer experience.
Let us explore a few recent examples of the power of analytics in the banking sector. While these examples are of banks outside India, they are equally applicable to Indian banks.
A European bank tried several retention strategies by focusing on inactive customers to counter a shrinking customer base, without any significant results. It then turned to machine learning algorithms that predicted which active customers are likely to reduce their business with the bank. This new understanding gave rise to a targeted campaign that reduced churn by 15 percent.
A US bank used machine learning to study the discounts its private bankers were offering to customers. Bankers claimed that they offered them only to valuable ones and more than made up for them with other, high-margin business. The analytics showed something different: patterns of unnecessary discounts that could easily be corrected. After the unit adopted the changes, revenues rose by 8 percent within a few months.
A top consumer bank in Asia enjoyed a large market share but lagged behind its competitors in products per customer. It used advanced analytics to explore several sets of big data: customer demographics and key characteristics, products held, credit-card statements, transaction and point-of-sale data, online and mobile transfers and payments, and credit-bureau data. The bank discovered unsuspected similarities that allowed it to define 15,000 microsegments in its customer base. It then built a next-product-to-buy model that increased the likelihood to buy three times over.
While data analytics has already started showing results in other countries, the Indian BFSI sector is now betting big on data analytics to increase organizational success, increase returns, transform business operations, reduce risks and provide seamless services to its customers. The usage of data analytics in India has vastly improved over the past few years and obtaining customer intelligence has become simpler. According to NASSCOM, the big data analytics industry in India is expected to reach $16 billion by 2025 from the current $1.2 billion. By deploying analytics, the BFSI sector aims to solve three major business challenges – performance, risk accessibility and profitability. A data-driven approach will help derive actionable insights and achieve persistent and cost-effective customer acquisition. Banks are using data analytics to predict mortgage default risk and customer authentication.
A few data analytics trends that banks are looking at today are:
Predictive analytics: Predictive analytics uses current and historical data to predict unknowns like future customer behaviour and activity. For instance, it offers insights into the possibility of customer propensity towards a personalised credit card offer. Its potential is brought out most efficiently when integrated with business processes to draw analytic insights, generating projections that can be used automatically by other systems like a BPM or a CRM system.
Adaptive analytics: Customer-specific strategies are adapted automatically to address customer responses in a better way during live customer interactions, allowing real time optimization.
Data visualization: Many organizations hold vast amounts of data, but struggle to discover its business value. Emerging business intelligence and data visualization capabilities help companies provide actionable intelligence and find discernible patterns within large datasets. Firms can now integrate advanced interactive visualization capabilities within their CRM and digital process automation (DPA) solutions, to uncover hidden patterns, and make smarter business decisions.
A key area emerging is that banks and major FIs are setting up dedicated analytics and AI Centre of Excellences (CoEs) in strategic partnerships with fintech and knowledge mentors to drive the innovation story forward.
The following areas of banking are facing disruption as the industry rapidly deploys AI and analytics:
Banks are adding custom services and offering better channel experience to customers to grow their top line. Many banks are introducing chat bots backed by AI abilities, which can understand the emotions of the customer by analyzing their voice and facial expressions and converse accordingly. Through big data and machine learning, these “bots” know how to respond to the customer’s queries – from onboarding concerns to transaction-specific questions. Additionally, the technology is capable of managing customer requests and making product recommendations. The key benefit of this advancement is to attract the tech-savvy millennials, known to prefer less human interaction when it comes to financing. Another benefit is to promote less pushy contextual nudges and next best action that customer could take on the basis of their geographical location and their latest financial interaction.
Investment Advisory – Digitization of Advice
Digital-advisors are changing the investment landscape, with AI-powered platforms automating relationship heavy private banking and asset management areas. Introduction of Digital-advisors almost entirely eliminates financial advisors and relationship managers from the investing process. Investors no longer have to shell out wealth or pay hefty fees for something they might not want or need.
Now, Digital-advisors collect information about an investor’s financial goals and the level of risk they’re willing to incur. This data is further integrated with the macroeconomic data and fed into algorithms (with quantum computing we will be able to run more sophisticated algorithms in future). In turn, the results are used to offer investment advice to the individual, allowing him/her to make educated investment decisions. In many cases, the digital-advisor will fully automate the purchase and management of investments.
Fraud detection and risk management
AI can detect fraud before it happens. Technology can rapidly mimic the thought process of a human analyst to review each transaction in every portfolio at a bank (big data stream). AI enables banks to not only be alerted to potential fraud but also gives the likelihood of a card ever becoming compromised. The appropriate usage of machine learning algorithms could result in the reduction of false positives which not only improves the efficiency of the AI/ ML Fraud Detection process but also helps in improving customer satisfaction. One of the biggest uses of data analytics is in supporting banks in managing risks, by providing real time alerts if a risk threshold is getting surpassed.
The financial industry is a heavily regulated industry with the rapid evolution of the laws and regulatory mandates. AI can “learn”, remember, and comply with all applicable laws – from KYC and anti-money laundering regulation to laws governing asset management. This results in the elimination of human errors, identification of complex patterns and financial institutions to meet their regulatory obligations.
Operational Optimization & Financial Management
Operational analytics solutions transform big data into insights for improved decision-making in near real-time, lower costs, and enhanced service at granular levels. Banks can outperform the competition with in-depth analysis of the performance of key operational areas, such as sales and operations planning. Analytics also builds models related to demand forecasting, inventory management, network optimisation, and HR (human resource) operations across the banking infrastructure, including its branches and various departments. Big data analytics deployed across financial dashboards and statements give a detailed, data-driven view of the financial picture. Financial KPIs help answer specific business questions and to forecast possible scenarios, analyse performance against budgets, real-time monitoring of financial indicators, and so on. This helps bottom-up accountability as well as supports timely decisions based on the data-driven insights.
Rethinking Talent Strategies
The key traits financial institutions look for when hiring talent are: quantitative and technical skills as well as business acumen to generate insights. There’s a fundamental rethink in terms of people strategy with most financial organizations fostering a culture of innovative thinking and embracing hacks for hire to bolster their human capital. In order to meet the growing talent demand, financial organizations are investing in developing robust learning modules and skill development programmes to reskill staff. Many leading financial organizations are taking an active step to partner with institutions for training and sourcing talent. This is in a way, paving the way for talent exchanges. Advanced People Analytics is helping the HR department to redefine people strategies in the areas of recruitment, retention, talent management, compensation and cost management, thereby directly impacting the business process.
Corporate Training As A Key Indicator of Changing Trends in the Financial Sector
There has been a significant shift in mindset when it comes to upskilling. Earlier the focus was on the upskilling of a few isolated people in a core data science team. However, organizations have now begun to realize that in order to derive competitive advantage from data, every employee must become data literate and “data smart”. There has been a strong demand for analytics training across levels and training at scale. Taking a top-down approach, this has spanned across an appreciation program for senior leadership, followed by a more hands-on training for the data enabled roles (basically, anyone in the organization who has access to or works with any kind of data) and finally, specialized and more advanced training for the existing data science team.
Functions across banking and finance (risk, marketing, operations, collections, regulation, governance, reporting etc) have begun to increasingly rely on big data analytics to optimize their performance. This has resulted in an increasing demand for data scientists who are highly specialized. In particular, Machine Learning has become a dominant force and there is significant demand for hyper-specialized skills in Machine Learning and Deep Learning.
There is an emergence of the analytics hub, like a center of excellence for analytics. The hub consists of a pool of data scientists who work across functions and do the heavy lifting. A centralized team puts the data scientists in a large group where best practices are easy to share and every data scientist is exposed to new skills a lot faster than when they are in smaller, siloed teams. Typically, the smaller, faster, usually non-strategic kind of analysis can be done within the smaller, internal teams and the bigger, more strategic analysis goes to the centralized team.
The Way Forward
Judging the impact of artificial intelligence and analytics on job roles and skill-set, and how it will redesign jobs frameworks, (for eg. AI’s robo-advisors are replacing financial advisors), financial organizations are now doubling down on closing the talent gap by collaborating with leading institutes and stakeholders to even out the supply and demand gap. In an automated world, financial organizations have recognized the changing skills demand and in order to keep pace with the market, they are teaming up with stakeholders to develop tailored solutions for talent management and develop work-ready workforce.
The disruption caused by AI and analytics is increasing exponentially and is geared up for making a great economic impact. From better customer experience to investment opportunities for the common man, from predicting fraud to mitigating investment risk, AI and analytics has the potential to revolutionize the industry and improve the financial health of millions of people.
Banks are making significant investments in technology and infrastructure, while integrating big data analytics and AI within their systems for competitive advantage. So, are you geared up for this revolutionary change?