Sunday, 22 May, 2022
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Data Science: Game Changer in Digital Transformation of Banking Sector

Md. Latiful Islam

Data Science: Game Changer in Digital Transformation of Banking Sector
Md. Latiful Islam

Let me start with the quotation of Bill Gates – “Banking is necessary, but banks are not.” I shall not say anything here in favour or against what Bill Gates as it is difficult to imagine a world without banks. It has been integrated into our everyday lives and total economic system will be collapsed without having banking services. However, due to massive improvement in the financial technologies, we frequently hear buzzwords ‘FinTech” and ‘Digital Transformation’. Fintech is the technology-based financial services that provide several banking services in parallel with the banking system. I am not going to compare how fintech is being considered as a disruptor to banking. Rather, I shall focus here on how Digital Transformation can bring new customer experiences in our banking sector.

We are passing through Industry 4.0 era that implies the transition from a time when people worked with computers to a time when computers work without human. The widespread use of computers, the internet and mobile phones along with the development of information technology have had a significant impact in the financial sector to introduce financial instruments and products. We may mention some names of technologies like Artificial Intelligence, Machine Learning, Block-chain, Internet of Things and Augmented Reality those developed the robotic process automation in the banking system. All these technologies use huge internal and external data to create the algorithm based on which it run. The finished output depends on the quality of data fed in the process; i.e. garbage in, garbage out. This is the challenge in this ecosystem and Data Science entered in the process to give the flawless output. 

Nowadays, few banks in our country claim that they have done huge digital transformation in their banks. Yes, technologies brought huge improvement in our banking industry. However, with my little knowledge on the global digital transformation, my understanding is that sometimes we make confusion in Automation and Digital Transformation. Automation is the technique of using technologies to simplify the existing processes to produce and deliver goods and services with minimal human intervention. Whereas, Digital Transformation is the technique of using digital technologies to develop a new, or modify existing business processes, philosophy, and customer experiences to meet new business and market requirements. We have made huge automation but started journey of digital transformation in our banking sector. I shall try to present how Data Science may be the game changer in the digital transformation of banking sector.

In my graduation days in Statistics in 90’s, I learnt Data being a set of values of qualitative or quantitative variables about one or more persons or objects. Now, spectrum of data covered everything in the world. An American Professor W. Edwards Deming defined Data as “In God we trust, all others must bring data”. As per the definition, data may be classified as structured, semi-structured and unstructured forms. Unstructured data includes images, audio and video files. In the world, volume of unstructured data is much higher than structured data. All together, concept of Big Data is very vital in the field of Data Science.

In the journey of digital transformation, the banking industry across the world has been catching up quite well in blending advance analytics with Robotic Process Automation (RPA) implementation in its business process. Human time is too expensive to be wasted in carrying out routine and repetitive tasks. To gain competitive advantage, banks must acknowledge the crucial importance of data science, integrate it in their decision-making process, and develop strategies based on the actionable insights from their client’s data.

Data and advance analytics are proving to be a huge differentiator in most of the businesses, primarily in banking and financial services. Business decisions related to revenue, cost control, customer relationship, products marketing, risk management etc. mostly revolves around organisations’ data management capability. Efficiency of this management depends on the data quality, data structure and integration of the internal systems.

Many banks have already shifted to data warehouse management equipped with fast and parallel processing capability. Most of them have started augmenting data warehouse with Big Data capability to embrace both structured and unstructured data. Data management capability helps business draw insight and carryout analysis for better informed decision. In parallel, we have been appreciating radical evolution in analytics space from traditional business intelligence to the advance analytics. Advance analytics explores deeper in the data system to find out avenues for new revenues, area to cut operational cost, eliminate redundant process, identify potential area of automation etc. while redesigning the new business model.

Let us see some simple transformations of the regular processes in the banking sector using internal and external data. All these transformations have brought new customer experiences over the traditional banking system.

Fraud Detection: An example of efficient fraud detection is when some unusually high transactions occur and the bank’s fraud prevention system is set up to put them on hold until the account holder confirms the deal. Fraud detection algorithms can investigate unusual transactions based on customers’ transaction patterns and condition put in the system. For new accounts, it may detect multiple accounts opened in a short period with similar data. Fraud Detection system in the card transactions system triggers at the event of potential fraud transaction reduces huge efforts in post facto manual checking.

Customer Segmentation: Customer segmentation means singling out the groups of customers based on either their behaviour (for behavioural segmentation) or specific characteristics (e.g. region, age, income for demographic segmentation).

There is a whole bunch of techniques in data scientists’ collection such as clustering, decision trees, logistic regression, etc. and, as a result, they help learn the Customer Lifetime Value (CLV) of every customer segment and discover high-value and low-value segments.

Personalised Marketing: Data scientists utilise the behavioural, demographic, and historical purchase data to build a model that predicts the probability of a customer’s response to a promotion or an offer. Therefore, banks can make an efficient, personalised outreach and improve their relationships with the customers.

Customer Support: As a part of customer service, customer support is an important but broad concept in the banking industry. In essence, all banks are service-based businesses, so most of their activities involve elements of service. It includes responding to customers’ queries and complaints in a thorough and timely manner and interacting with customers. In order to stay ahead of their competitor, banks are using analytics to help customer support agents to be efficient at their jobs, decreasing resolution times and improving overall customer experience.

Customer Lifetime Value: Acquiring and retaining profitable customers is an ever-growing challenge for banks.                

As the competition is getting stronger, banks now need a 360-degree view of each customer to focus their resources efficiently. This is where the data science comes in. Lifetime Value (LTV) is a measure of how long organisations are able to retain their customers. LTV is used by many banks as a direct measure of customer satisfaction. In this digital age, LTV becomes important in the banking that gives a greater emphasis on acquiring and retaining customers. With predictive analytics, banks can know which customers to focus on for new engagement efforts.

Managing Customer Data: Currently, digital banking is becoming more popular and widely used. This creates terabytes of customer data, thus the first step of data scientists team is to isolate truly relevant data. After that, being armed with information about customer behaviours, interactions, and preferences, data specialists with the help of accurate machine learning models can unlock new revenue opportunities for banks by isolating and processing only this most relevant clients’ information to improve business decision-making.

Recommendation Engines: The rise of e-commerce and retail industries has improved the skill of recommendation engines. In the banking sector, recommender systems are being employed to identify behavioural patterns and recommend services or products to the targeted segment of customers.

Real-time and predictive analytics: Machine learning algorithms and data science techniques can significantly improve banks’ analytics strategy since every use case in banking is closely interrelated with analytics. As the availability and variety of information are rapidly increasing, analytics are becoming more sophisticated and accurate. Real-time analytics help to understand the problem that holds back the business, while predictive analytics aid in selecting the right technique to solve it. Significantly better results can be achieved by integrating analytics into the bank workflow to avoid potential problems in advance.

Credit Scoring Application: Credit scoring tools that use machine learning algorithms are designed to speed up lending decisions, while potentially limiting incremental risk. Customers’ transactions and payments history from financial institutions served as the foundation of most credit scoring models. These models use tools such as logistic regression, decision trees models, and statistical analysis to generate a credit score using limited amounts of structured data. Banks and other lenders are increasingly turning to additional, unstructured and semi-structured data sources, including social media activity, mobile phone use and text message activity, to capture a wider view of creditworthiness and improve the rating accuracy of loans.

Client-facing Chatbots: We already familial with Siri of Apple, Google Assistance of Google, Watson of IBM. All these are Chatbots (short for of ‘Chat Robot’) those use Artificial Intelligence (AI) and natural language processing (NLP) to understand customers’ instructions/questions placed through voice or text and simulate them to automatically give the responses or process the instructions. In our country, a few banks implemented Chatbots which basically are providing balance information or alerts to customers, or answering simple questions. Besides these services, advanced chatbots may be used in funds transfer, utility bills payments, service requests, information related to trade services and more automated query responses of call centres.  

In some modern countries, through implementation of new devices and advanced technologies, traditional banking has started moving towards virtual banking. On the other hand, experience of Covid-19 has raised up our requirements and accelerated some disruptive digital transformations in the banking sector to meet customers’ new requirements in the crisis period and to bring ease in providing services for the banks being the service provider. 

Data, as we know, has been the driving factor for many sectors, especially in the banking and financial organisation. However, outcome of the data-driven activities depends on the quality of related internal and external data. Data management is also important. So, organisation like banking and financial institutions should put focus on cleaning of customer data, integration of the internal systems and building data warehouse to develop advance data-driven technologies that shape up the real sense of digital transformation. Data science can help to get optimum results in the endeavour of data management being the game changer to achieve the goal.

 

The writer is the Chairman, FinPro Consultants Limited