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2022’s Go-To Guide to Data Analytics in Banking & Financial Services

2022’s Go-To Guide to Data Analytics in Banking & Financial Services

Compounding high costs of bad data are the opportunity losses banks risk with slow efforts to scale their Data Analytics function.

Not as a set of discrete projects, data analytics must evolve into an actual business discipline. The imperatives to do so are the twin drivers: advancing technologies (the exponential growth in meaningful data and available computing power) and enormous economic pressure banks face today.

Three ways data analytics generate an increase in bank’s profits.

  1. Amplify P&L levers (accelerate growth, enhance productivity, and improve risk control)
  2. Find new sources of growth (creating new business models, e.g., offering data analytics with others in the partner ecosystem)
  3. Deliver on the promise of a digital bank (enhanced omnichannel experience at lower costs)

Before analytics is applied to structured or unstructured sources, financial organizations have to resolve industry-specific challenges (regulatory requirements, data security, data quality, and data siloes).

Today leading banks leverage the power of analytics in more ways than one. One uses machine-learning algorithms that predict currently active customers who might drop business. Another use is to analyze competitor campaigns that curb any unnecessary discounts banks may be offering. Yet, another uses analytics to parse big data to discover microsegments in its customer base to create that next-product-to-buy.

2022 priorities for getting more out of data analytics investments.

  1. Post-crisis, as banking analytics use-cases increase across sales & marketing, HR, Risk & Compliance, and IT, banks will get more bang-for-their buck as they align analytics priorities to strategic vision.
  2. The second boost would come when managers scale analytics pilots by augmenting technical production and engineering capabilities. To succeed would mean to absorb data-driven iterations into work rhythms, something that change management programs can help in.
  3. The third priority concerns staffing. Individuals chosen for analytics roles (data engineers, scientists, ML engineers, e.g.) must bring a collaborative mindset.
  4. Financial organizations will

create value beyond the logical use-cases (digital marketing, transactional analysis, cybersecurity) by exploiting rich data sets by synching data across organizations and finding innovation breakthrough areas.

All said and done, much of these priorities would be possible when banks use robotics to eliminate 20% – 40% transactional accounting work. Not only will this allow finance teams more time for decision-making, but it also helps them gauge how best predictive analytics meshes with the performances they seek.

Banking analytics as it plays across the selling process.

Senior managers tasked with banking operations, and profitability must step back from the customer life cycle to tease out interlocks where analytics brings information and value. It is discussed below with the corresponding analytics benefit.

  • Customer Identification and acquisition (acquisition analytics and campaign design)
  • Customer relationship management (managing portfolio and meeting transactional needs)
  • Customer cross-sell (need analysis, demography, credit history analysis, next-best-product)
  • Customer retention (churn prediction, lifetime value modeling)
  • Customer value enhancement and increasing wallet share (behavioral segmentation, product affinity modeling, and differentiated pricing)

As the saying goes, “Future is already here; it’s just not evenly distributed.” Banks will need to smooth out customer road maps so that each one receives high-quality and personalized relationships.

How does all this tie-up to 2022 predictions for banking data analytics? 

A recent study of 10,000 companies reported that 71% are in the midst of or stand at the edge of disruption. On a 0 to 1, banking moved from 0.43 in 2011 to 0.52 in 2019. In 2020, 1 in 5 banking and payments sectors players were less than 15 years old.

The reasons are familiar – disruptive Gen Y expectations, Fintech entries, accelerated digital banking, and shifting regulation.

Given the mandate – ‘transform or make way’ – no two banks would (or should) approach data analytics the same way.

Practical steps for chasing analytics transformation and the next frontier.

The reasonable steps will remain the same as before: identify business problems, centralize data, automate processes, focus on decision making, optimize finance cycles, fight for talent, and drive continuous improvement. The innovators will turn their data analytics engines in pursuit of three strategic objectives:

  • Reinforcing the core (augment existing core bank offering)
  • Creating a new distribution channel (becoming a preferred partner to third parties)
  • Launching innovative ventures (develop new businesses and business models)


In the final analysis, as the fog lifts over the current crisis, data analytics in banking will pay dividends for players that use it for intelligent pricing, selling, retention, and intelligent prospecting.


Customer analytics 2.0 for banking

Customer analytics 2.0 for banking

Customer analytics use cases in banking extols the omniscient algorithm, but underestimating clean, correct, complete data invites risks. So how does the new-age C-suite Banker work out their priorities, asks Muraleedhar Ramapai, executive director at Maveric Systems

To bring innovative products faster to the market, global financial enterprises seek cleaner real-time, actionable data. But it’s more easily articulated than achieved. In fact, our present-day predicament mirrors the legend of El Dorado. For the better part of four centuries, the lore of El Dorado changed from being a man, to a city, to a kingdom and finally to an empire. The legend launched multiple expeditions across an entire continent and influenced popular culture. The lure of gold snared generations.

Not unlike, the spell data-led disciplines (AI, ML, DL and Data sciences) cast on banks today. This time though, enterprises are better prepared to not fall victim to the El Dorado myth.

Or are they?

The data conundrum and the diamond connection

Gartner’s 2021 D&A top trends notes how emancipated organisations’ are using various solutions to address high levels of diversity, distribution, scale and complexity in their data assets. As top banks invest more in the ‘diamond’ technologies – scalable AI, composable D&A, data fabric, X ops, graph technologies, and edge computing – it will help for CIOs, CDOs and CXOs to discern how the basic ‘carbon’ (data) transforms to become the ‘glitter’ (AI-led solutions).

After all, isn’t it easy to take a diamond’s shine for granted? Like perhaps it is to overlook processes that make data innovation-worthy?

For one, diamonds are increasingly cut in sophisticated factories with high tech equipment rather than by hand. Next, the diamonds are sorted in the rough, planned for manufacture, cleaved, or sawed into preliminary shapes, its girdle shaped, and the facets polished. Needless to say, decisions at each step influences the shape, size, cut, colour and clarity of the final gem and consequently its value.

But like data, analysing raw diamond batches is arguably the trickiest step in the cutting process, requiring the most experience and technological expertise. Does a craftsman cut one large round shape that sells for more per carat but wastes more of the raw stock? Maybe, two smaller cuts that sell for a lower price but waste less rough? What combination is likely to present the best yield? In the event, all calculations centre on one decision: How does one maximise the market value of possible gems that can be produced from a starter batch.

Come to think of it, these decisions are analogous to ones banks make about data. But when these decisions are taken sub-optimally, there are costs to pay.

The challenges and costs of taking data for granted

The problem for financial enterprises, simply put, starts with: “We have too much data and it piles on relentlessly, unstructured and in multiple formats”. And ends in the premise: “We need to extract faster and richer customer insights”. In between those two poles, is the cause-continuum that blocks data exploitation. Be it the lack of data access, data integration complexities, data stagnating in siloes, poor data quality, opaque data governance, or the thorny challenge of sharing data between various cloud configurations (public and on-prem cloud); not to mention adherence to changing security and regulatory mandates – most organisations face deep challenges in ingesting, integrating, analysing, and sharing data.

As the amount, types and sources of data increases, the challenge only snowballs. The stakes grow deeper. Not more than three percent  of companies’ data meets basic quality standards. Research shows that 74 percent of data is not analysed in most organisations, and up to 82 percent of enterprises are inhibited by data silos. There are human costs as well. Their primary job dissatisfaction, as data scientists claim, comes from spending most of their time massaging rather than mining or modelling data. In fact, a leading AI company cites 55 percent of the surveyed data scientists saying the quality and quantity of training data poses the biggest challenge in their jobs.

So how does all the bad Big Data (inaccurate, incomplete, inappropriate) add up economically? Well, if you consider multiple sources the detrimental effects pile up.

  • Gartner’s survey conducted across 500 organisations estimates that every year, poor data quality (DQ) costs organisations an average $15m
  • MIT Sloan reports employees waste 50 percentof their time coping with mundane data quality tasks

Data scientists are held back by data, not the science, so what are the possible solutions?

Depending on a Bank’s DQ maturity, a quick answer may include – defining adequate data standards and then establishing it across the enterprise, strengthening data governance by including DQ dashboards, and D&A leaders leveraging diverse groups (vendors, service providers) to exchange alternate perspectives on best practices and insights.

The other dynamic that governs customer analytics, is that fresh implications surface with each technology wave. Take for example, the dependence AI-first banks have on data.

For these new-age enterprises reimagining customer engagement across diverse platforms and partner ecosystems, doesn’t begin by building a highly flexible and fully automated decisioning layer via an AI-powered capability stack. To manage data accuracy, they have to first reimagine their entire pipeline with its integrations. It is only then can highly personalised and timely tailored communication be delivered through preferred channels that builds loyalty and maximises customer lifetime value.

All that glitters is gold – when mined correctly

How then does the new-age C-suite banker work out priorities?

The eye-blinding lustre of AI’s much-touted power notwithstanding; CIO’s, CDO’s and CXO’s have to maintain laser focus on data management, particularly its lifecycle – starting with data discovery, definitions, lineage, quality, and its frictionless access.

After all, if the diamond industry teaches us a lesson – it is that skills of a master craftsmen can only shape the ‘exquisite’ and ‘priceless’ after the pipeline processes push through the best quality of raw rocks.

Disclaimer – This article is originally published on Bobsguide