WHY EXPLORE THIS SUBJECT
The Utiva Data School is designed and structured to help professionals accelerate data science learning and support the scale of data capability mobilization for the growth of global businesses. One key field data has benefited from this learning program is the banking sector. With more than 150 students from more than 9 banks in Nigeria, the Utiva Data School remains the leading learning program for Data Science and the Banking Sector
Utiva Data School X Global Banking Sector
INTRODUCTION
The banking sector in Nigeria is one of the foremost and important sectors in the Economy which started as far back as the Colonial Era and has evolved into what we have today. This evolution has brought about a great need for advanced technology, automation, faster processes and the need for enhanced security.
The advent of technology has made Banking processes faster, more reliable, convenient above all customer-oriented with. The volume of data being processed has risen considerably and the need for proper analysis and interpretation of this huge data has risen as well.
As banks struggle to make profits and remain liquid, they need to understand their customer’s needs and offer products designed to suit them and remain competitive. To achieve this, banks need to move from the basic debit and credit and dwell deeper into analytics, data mining, predictive analysis to enable them to acquire, manage and achieve high customer retention. Data Analytics in the banking sector has enabled banks to improve on the services being offered to their customers by designing products tailored to meet the needs of customers, create and provide content and strategies to enable them to serve customers better, which ultimately increases revenue.
We need to ask a question as to why banks are moving towards big data analytics. Probably for proper risk management, a diversion into other markets, regulatory reforms, changing business models, operational efficiency increased profitability, competition.
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THE BRICK, MORTAR AND PAPER OUTFIT
For as long as civilization existed, banking has always existed side by side and grown with civilization. In fact, even before civilization began, bank-like systems already existed.
The first banks, which sprung up from ancient Mesopotamia, started in the form of financial lending of seeds to farmers and at harvest, farmers pay back their seed loan from their harvest.
This period transitioned into the Medieval period of banking were like the previous period, most of the banks were merchant banks.
The modern banking period began in the 17th century (1601–1700). This era paved the way for the sophisticated banking system currently being experienced. During this period, the banking system was only “brick, mortar and paper”. Bank customers must walk into any bank outfit and take a deposit or withdrawal slip to be able to perform their transactions. This era was characterized by manual processing of records.
The brick and mortar outfit lasted till the 21st century and then we moved to an exciting and interesting virtual world. Not unlike other service industries, traditional banking finds itself grappling with a new world order in which clients expect that money like everything else in their lives is available at their fingertips. With the growing popularity of person-to-person money transfers, payment apps and digital wallets, traditional banks’ survival demands nothing short of a revolution in how they interact with their increasingly tech-savvy clientele.
ADVANCED ANALYTICS IN THE BANKING WORLD TODAY
Banking and the Financial Services Industry is a domain where the volume of data generated and handled is enormous.
Adopting advanced analytics and inculcating it into the existing banking environment is one of the key elements of surviving in this digital era.
Data gives valuable insights into user behaviour and helps to optimize customer experience accordingly. Imagine having a complete customer profile and exhaustive data on product engagement at hand, you can predict and prevent potential risks. Banks use big data to get to know their customers and, as a result, find new ways to cater to them, connect in a more meaningful way, and deliver more value. They segment their customers through their profiles and determine what banking products to cross-sell and upsell using their profiles. They use data to identify the main channels where the customer transacts like credit/debit card payments and ATM withdrawals, discover their spending patterns and make customized products to suit different customers. This leads to a personalized customer experience and personalized marketing. Banks also use big data to improve their customer service delivery based on customer feedback.
Banking is becoming automated through technology, and the key to this lies in big data. Technology has greatly improved business processes. The advent of ATMs, online banking, bank mobile apps, Robot-calls, USSD shortcodes etc. have proven to be far more efficient than the traditional banking that we used to know as it reduces the time needed to perform a lot of banking activities and significantly decreases the human error associated. People can now
take loans or make investments from the comfort of their homes because processes like credit scoring and investment banking are automated.
Most banks also use real-time machine learning and predictive modelling to analyze big data to pinpoint fraudulent behaviour and minimize financial risk for online banking providers. With advanced analytics, banks can predict customer behaviour and identify suspicious spending patterns. The alerts are sent out in real-time, impeding further fraudulent activity with quick actions (freezing the account and alerting the customer).
THE FUTURE OF BANKING AND ADVANCED ANALYTICS
The future of banking will look very different from today. Faced with changing consumer expectations, emerging technologies, and new business models, banks will need to start putting strategies in place now to help them prepare for banking in the future.
Below are key trends that will change the Banking sector going forward
As the number of electronic records grows, financial services are actively using big data analytics to derive business insights, store data, and improve scalability. It has been estimated that Bank transactional data will grow by 700% in 2020 and will form a major part of the Big data disruption in the coming years.
The Banks can anchor their strategies based on the following pointers:
- Customer SegmentationCross-selling and up-selling
- Feedback mining and adequate interventions
- Proactive risk assessment
- Discovering spending patterns of the customers
- Identifying the main channels customers transact with.
- Personalize product offerings
DATA INTEGRITY AND ENTERPRISE ANALYTICS
The biggest unexploited opportunity in Banking analytics is the lack of ‘connectivity’ within the enterprise. For example, most financial institutions have built out separate analytics practices including marketing and digital (web, social, and mobile) analytics, credit risk analytics, operations analytics, fraud analytics and course compliance analytics. However, these teams are largely ‘siloed’ in their activities. There is a need to have an ‘Analytics Centre of Excellence’ to drive all the data needs of the organization; from strategy to
insights.
DIGITAL AND EMERGING TECHNOLOGIES
New technologies are gradually changing the way banking is done and perceived. From back to the middle to the front office, disruptions are happening faster than imagined. Cloud banking has become one technology banks can’t do without. In 2017, 74% of financial services companies according to Forbes have adopted some extent of hybrid/public cloud architecture. Many banks are drawn on whether to move their core banking applications to the cloud owing to the increasing cyber threats within the sector.
CYBER RISK AND FINANCIAL CRIME (CYFI)
The financial crimes ecosystem is evolving as criminals adopt new innovative ways of committing crimes. A research done by the University of Maryland indicates that there is a hacker attack every 39seconds in the United States of America Banks need to embrace advanced technologies such as analytics and artificial intelligence to improve threat visibility and detect fraud effectively
THE DAVID AND GOLIATH MARRIAGE
Future-focused banks are looking beyond the rivalry with fintech and brazing up for the inevitable marriage that must happen between the Large, Inefficient big banks and the Nimble, efficient small fintechs. Collaboration is the key between the fintech and banking industries. A recent survey by PwC found that 82
percent of banks, insurers and asset managers intend to increase the number of partnerships they have with fintech firms over the next three to five years. Banks have a customer base while fintech has cheap and efficient service solutions. The two can benefit by working together. Both banks and fintech have common goals. A typical example is the case of Wema bank and Alat. Launched in My 2017, Alat operates as a truly digital Bank. Was designed as a fintech but relies on the customer base of Wema to succeed.
CONCLUSION
To gain competitive advantage, banks should recognize the importance of data science, incorporate it in their decision-making process, and develop strategies based on the actionable insights from their customers' data. Start with small, doable steps to integrate data analytics into operating models and stay ahead of the competition.
