1. Banks’ futures depend on institutions’ ability to master three key things – customer experience, compliance, and storage.
2. strategic default models were a waste of time and money, not because they were inaccurate, but because the parties involved failed to anticipate how offering one customer an attractive loan restructure would influence other customers who hadn’t yet defaulted. The analytics didn’t fail. The implementation strategy did.
CATCHING UP
1. Banks that don’t prioritize building and sustaining a happy customer base risk being relegated to the commodity pile.
2. And those that fail to comply with stiffening global privacy rules risk fines, scandals, business losses or a combination of the three.
3. That leaves storage. While storage isn’t a measure of effectiveness, it is a critical enabler of success. Without a steady resource of dependable, accessible, affordable storage, organizations can’t make ends meet, never mind execute on aggressive business goals. This is particularly true for companies in financial services, arguably the most data-intensive sector in the global economy.
DATA STOREAGE
1. Data storage is growing exponentially across sectors. In 2007, the global capacity for data storage was 281 exabytes.
2. That number increased 100-fold over the next decade, up to 33 zettabytes in 2018, and a report by IDC projected volumes would keep growing about 40% a year, hitting 175 ZB by 2025.
3. Data-hungry initiatives are going to continue to push storage needs higher in financial services.
4. Finance has long been built on the ability to amass large amounts of customer data. But data collection trends have intensified in recent years.
5. Customer experience and compliance needs are exploding, and applications ranging from blockchain to high-speed trading to advanced fraud detection all require huge amounts of data to operate at peak efficiency.
6. These evolving trends have pushed financial services organizations to reexamine the hierarchy of data and technology. In the past, processing was concentrated at the server level with more and more storage getting added on as data requirements grew.
7. Today, storage has moved to the center of the wheel while servers play a more peripheral role in organizations’ efforts to meet the exponential growth in data mining.
CUSTOMER EXPERIENCE
1. No single factor has propelled the financial services sector’s use of data like the impulse to take care of customers.
2. Banks, in particular, are in a constant struggle to differentiate themselves from competitors offering similar product lines – loans, savings accounts, insurance plans, money management vehicles – with similar terms.
3. Rather than compete just on price, banks need to compete on smarts – tapping data to understand their customers better, convince them to buy more and do so at a better price point for the bank.
4. Banks also need data to level the playing field against a new set of competitors. Upstart fintech players are creating hyper-efficient, cloud-first businesses offering new apps, processes, products or business models – all online.
5. These fintechs are raising the bar for customer experience, forcing banks to make more strategic investments in storage, data analytics, and overall customer service.
6. Data that used to be collected and regularly disposed is now retained for longer periods of time to ensure that banks have every angle of the customer relationship covered.
7. This requires immense amounts of storage, access to third-party data and a highly agile mechanism for retrieving key informational nuggets exactly when they’re needed.
DATA ANALYSIS
1. For example, segmentation of customers based on available data allows a bank to perform predictive analysis for a particular customer’s next purchase.
2. The bank knows a targeted customer travels frequently – based on prior credit card purchases – so it offers a new card with miles benefits and discounts for airport services.
3. The customer is pleased that the bank understands their needs and presented an offer that actually served a purpose.
4. Rich troves of data can unearth cross- and up-selling opportunities based on customer insights and current customer behavior.
5. Surfacing the right data can trigger notifications in case, for instance, the customer has been investigating car loans on the internet, has an expiring term deposit, is living in a home that’s currently for sale or is renovating a house.
6. The banks can meet the customer’s demands at the lowest cost point, maximizing the value of each transaction.
COMPLIANCE
1. Regulatory pressure is forcing banks to collect, retain and report more data than ever before. Introductions of new regulations – everything from Basel III for leverage ratios to AML/KYC for anti-money laundering to FATCA for tax collections – force banks to disclose more granular information to central banks and regulators.
2. Banks also have to collect data in a more controlled way, so they can report it automatically and also make it available in case regulators make ad-hoc inquiries.
3. Then there are the issues with privacy. While certain rules put a premium on information transparency, regulations such as GDPR in Europe go the other way.
4. They force companies to get approvals before gathering personal data, shield certain data from oversight in certain circumstances, and spike other information when regulations stipulate that they do so.
5. This requires banks to set up a particularly agile storage infrastructure with nimble controls. While data can be a valuable asset, it can also be a liability if it’s not handled properly.
EXAMPLES OF BANKING ANALYTICS
1. Anti-money laundering (AML). Today, the vast majority of suspicious activity reports (SARs) are generated through manual transaction monitoring with scenario-based rules – and more than 90% of them could be generated automatically, without any human assistance, through the use of machine learning and predictive analytics. Paired with a sound implementation strategy that both executives and regulators are comfortable with, this advance in AML detection can not only capture new money laundering events missed by current transaction monitoring, but free existing compliance officers to focus on higher-value alerts.
2. Collections. Banks can use advanced analytics to divide delinquent borrowers into multiple – and extremely specific – segments. And by identifying certain segments as accounts likely to resolve on their own or with an automated reminder, they can improve their operational efficiency by not assigning those accounts to human collection agents. Again, a sound implementation strategy is critical. As with their AML counterparts, human collection agents can be reassigned to where their empathy and communication skills will make the largest difference.
3. Attrition. Every telecommunications provider struggles with customer attrition – which is why one of our clients responded by creating the Attrition Intervention Group, a “tiger team” of customer service specialists who are remarkably effective at convincing customers not to leave. The team’s secret? Analytics, which they use to focus on the right customers. In this case an investment in more accurate attrition prediction models paid off handsomely because not only did our client improve their ability to predict which customers could be convinced, they assigned their best specialists to them, ensuring those predictions were converted into “saves” at an impressively high rate.
https://www.datanami.com/2020/07/21/how-banks-can-compete-in-a-data-driven-future/
https://www.datanami.com/2019/11/01/start-with-the-end-in-mind-what-banks-should-consider-when-adopting-analytics/
