Customer Journey Towards Digital Banking

The bank branch as we know it with tellers behind windows and bankers huddled in cubicles with desktop computers, is in need of a massive transformation.

Today. most customers now carry a bank in their pockets in the form of a smart phone app, and  visit an actual branch is not really needed. But banks all over the world are still holding on to the traditional brick-and-morter branches.

Though many banks are closing these branches. In 2017 alone, SBI, India's largest bank closed 716 branches!

Today, despite all the modern mobile technologies, physical branches remain an essential part of banks' operations and customer advisory functions. Brick-and-mortar locations are still one of the leading sales channels, and even in digitally advanced European nations, between 30 and 60 percent  of customers prefer doing at least some of their banking at branches.

While banks like to move customers to the mobile banking platform, changing customer behavior has become a major challenge. The diagram shows the 5 distinct stages of customer behavior and banks must nudge customers to go along this journey.

4 Types of Data Analytics

Data analytics can be classified into 4 types based on complexity & Value. In general, most valuable analytics are also the most complex.

1. Descriptive analytics

Descriptive analytics answers the question:  What is happening now?

For example, in IT management, it tells how many applications are running in that instant of time and how well those application are working. Tools such as Cisco AppDynamics, Solarwinds NPM etc., collect huge volumes of data and analyzes and presents it in easy to read & understand format.

Descriptive analytics compiles raw data from multiple data sources to give valuable insights into what is happening & what happened in the past. However, this analytics does not what is going wrong or even explain why, but his helps trained managers and engineers to understand current situation.

2. Diagnostic analytics

Diagnostic Analytics uses real time data and historical data to automatically deduce what has gone wrong and why? Typically, diagnostic analysis is used for root cause analysis to understand why things have gone wrong.

Large amounts of data is used to find dependencies, relationships and to identify patterns to give a deep insight into a particular problem. For example, Dell - EMC Service Assurance Suite can provide fully automated root cause analysis of IT infrastructure. This helps IT organizations to rapidly troubleshoot issues & minimize downtimes.

3. Predictive analytics

Predictive analytics tells what is likely to happen next.

It uses all the historical data to identify definite pattern of events to predict what will happen next. Descriptive and diagnostic analytics are used to detect tendencies, clusters and exceptions, and predictive analytics us built on top to predict future trends.

Advanced algorithms such as forecasting models are used to predict. It is essential to understand that forecasting is just an estimate, the accuracy of which highly depends on data quality and stability of the situation, so it requires a careful treatment and continuous optimization.

For example, HPE Infosight can predict what can happen to IT systems, based on current & historical data. This helps IT companies to manage their IT infrastructure to prevent any future disruptions.



4. Prescriptive analytics

Prescriptive analytics is used to literally prescribe what action to take when a problem occurs.

It uses a vast data sets and intelligence to analyze the outcome of the possible action and then select the best option. This state-of-the-art type of data analytics requires not only historical data, but also external information from human experts (also called as Expert systems) in its   algorithms to choose the bast possible decision.

Prescriptive analytics uses sophisticated tools and technologies, like machine learning, business rules and algorithms, which makes it sophisticated to implement and manage.

For example, IBM Runbook Automation tools helps IT Operations teams to simplify and automate repetitive tasks.  Runbooks are typically created by technical writers working for top tier managed service providers. They include procedures for every anticipated scenario, and generally use step-by-step decision trees to determine the effective response to a particular scenario.

How Machine Learning Aids New Software Product Development

Developing new software products has always been a challenge. The traditional product management processes for developing new products takes lot more time/resources and cannot meet needs of all users. With new Machine Learning tools and technologies, one can augment traditional product management with data analysis and automated learning systems and tests.

Traditional New Product Development process can be broken into 5 main steps:

1. Understand
2. Define
3. Ideate
4. Prototype
5. Test

In each of the five steps, one can use data analysis & ML techniques to accelerate the process and improve the outcomes. With Machine Learning, the new 5 step program becomes:

1. Understand – Analyze:Understand User RequirementsAnalyze user needs from user data. In case of Web Apps, one can collect huge amounts of user data from Social networks, digital surveys, email campaigns, etc.
   
2. Define – Synthesize: Defining user needs & user personas can be enhanced by synthesizing user's behavioral models based on data analysis.
   
3. Ideate – Prioritize: Developing product ideas and prioritizing them becomes lot faster and more accurate with data analysis on customer preferences.
   
4. Prototype – Tuning: Prototypes demonstrate basic functionality and these prototypes can be rapidly, automatically tuned to meet each customer needs. This aids in meeting needs of multiple customer segments.Machine Learning based Auto-tuning of software allows for rapid experimentation and data collected in this phase can help the next stage.
   
   
5. Test – Validate: Prototypes are tested for user feedback. ML systems can receive feedback and analyze results for product validation and model validation. In addition, ML systems can auto-tune, auto configure products to better fit customer needs and re-test the prototypes.

Closing Thoughts

For a long time, product managers had to rely on their understanding of user needs. Real user data was difficult to collect and product managers had to rely on surveys and market analysis and other secondary sources for data. But in the digital world, one can collect vast volumes of data, and use data analysis tools and Machine learning to accelerate new software product development process and also improve success rates.

AI for IT Infrastructure Management

AI is being used today for IT Infrastructure management. IT infrastructure generates lots of telemetry data from sensors & software that can be used to observe and automate. As IT infrastructure grows in size and complexity, standard monitoring tools does not work well. That's when we need AI tools to manage IT infrastructure.

Like in any classical AI system, IT infrastructure management systems also has 5 standard steps:

1. Observe: 
Typical IT systems collect billions of data sets from thousands of sensors, collecting data every 4-5 minutes. I/O pattern data is also collected in parallel and parsed for analysis. 

2. Learn:
Telemetry data from each device is modeled along with its global connections, and system learns each device & application  stable, active states, and learns unstable states. Abnormal behavior is identified by learning from I/O patterns & configurations of each device and application.

3. Predict: 
AI engines learn to predict an issue based on pattern-matching algorithms. Even application performance can be modeled and predicted based on historical workload patterns and configurations

4. Recommend: 
Based on predictive analytics, recommendations are be developed based on expert systems. Recommendations are based on what constitutes an ideal environment, or what is needed to improve the current condition

5. Automate: 
IT automation is done via Run Book Automation tools – which runs on behalf of IT Administrators, and all details of event & automation results are entered into an IT Ticketing system

How to select uses cases for AI automation

AI is rapidly growing and companies are actively looking at how to use AI in their organization and automate things to improve profitability.

Approaching the problem from business management perspective, the ideal areas to automate will be around the periphery of business operations where jobs are usually routine, repetitive but needs little human intelligence - like warehouse operators, metro train drivers etc., These jobs follow a set pattern and even if there is a mistake either by human operator or by a robot - the costs are very low.

Business operations tends to employ large number of people with minimum skills and use lots of safety systems to minimize costs of errors. It is these areas that are usually the low hanging fruits for automation with AI & robotics..

Developing an AI application is a lot more complex, but all apps have 4 basic steps:
1. Identify area for automation: Areas where automation solves a business problem & saves money

2. Identify data sources. Automation needs tones of data. So one needs to identify all possible sources of data and start collecting & organizing all the data

Once data is collected, AI applications can be developed. Today, there are several AI libraries and AI tools to develop new applications. My next blog talks about all the popular AI application development tools.

Once an AI tool to automate a business process is developed, it has to be deployed, monitored and checked for additional improvements - which should be part of regular business improvement program.

How AI Tools helps Banks

In the modern era of the digital economy, technological advancements in Machine Learning (ML) and Artificial Intelligence (AI) can help banking and financial services industry immensely.

AI & ML tools will become an integral part of how customers interact with banks and financial institutions. I have listed 8 areas where AI tools will have the greatest impact.

Key Technologies for Next Gen Banking

Digital Transformation is changing they way customers interact with banks. New digital technologies are fundamentally changing banks from being a branch-centric human interface driven to a digital centric, technology interface driven operations. 

In next 10 years, I predict more than 90% of existing branches will close and people will migrate to digital banks. In this article, I have listed out 6 main technologies needed for next gen banking - aka the Digital Bank.

1. MobileMobile Apps is changing how customers are interacting with bank. What started as digital payment wallets, mobile banking has grown to offer most of the banking services: Investments, Account management, Lines of credit, International remittances etc., providing banking services anywhere, anytime!

2.Cloud & API

Mobile banking is built on cloud services such as Open API & Microservices. Open API allows banks to interact with customers and other banks faster. For example, Open API allows business ERP systems to directly access bank accounts and transfer funds as needed. Open API allows banks to interact faster, transfer funds from one back to another etc. In short Cloud technologies such as Open API and microservices are accelerating interactions between banks, and banks & customers, thus increasing the velocity of business.

3. Big Data & Analytics

Big data and analytics are changing the way banks reach out to customers, offer new services and create new opportunities. Today banks have tremendous access to data: Streaming data from websites, cloud services, mobile data and real time transaction data. All this data can be analyzed to identify new business opportunities - micro credit, Algorithmic trading etc.

4. AI & ML

Advanced analytical technologies such as AI & ML is increasingly being used to detect fraud, identify hidden customer needs and create new business opportunities for banks. Though these technologies are still in their early stages, it will get a faster adaption and become main stream in next 4-5 years.

Already, several banks are using AI tools for customer support activities such as chat, phone banking etc.

5. Biometrics & security.

As velocity of transactions increases, Security is becoming vital for financial services. Biometric based authentication, Stronger encryption, continuous real time security monitoring enhances security in a big way.

6. Block chain & IoT

IoT has become mainstream. Banks were early adapters of IoT technologies: POS devices, CCTV, ATM machines, etc.  Block chain technology is used to validate IoT data from retail banking customers. This is helping banks better understand customers and tailor new offerings to create new business opportunities.

Data Analytics for Competitive Advantage

Data Analytics is touted as 'THE" tool for competitive advantage.

In this article, I have done a break down of data analytics into its three main components and further listed down various activities that are done in each category.

Three Main Components of Data Analytics

1. Data Management
2. Standard Analytics
3. Advanced Analytics

Data Management

Data Management forms the foundation of data analytics.  About 80% of efforts & costs are incurred in data management functions. The world of data management is vast and complex, it consists of several activities that needs to be done:

1. Data Architecture
2. Data Governance
3. Data Development
4. Data Security
4. Master Data Management
5. Metadata Management
6. Data Quality Management
7. Document & Content Management
8. Database & Data warehousing Operations

Standard Analytics

Standard Analytics is what most businesses have been doing for a long time now. Standard business reporting, Alerts etc., There are several standard analytics functions that business needs for day-to-day activities.

1. Standard Reporting

2. Ad hoc queries

3. Data filtering

4. Alerts

5. Clustering

6. Trend Forecasting

7. Statistical Analysis

Advanced Analytics

Advanced Analytics is what getting all the attention. Big data & analytics using modern algorithms such as Map Reduce. In addition to big data, Advanced analytics includes newer technologies such as AI, ML, RPA etc.

1. Predictive Analytics

2. Prescriptive Techniques

3. Operations Optimization 

4. Simulation Modelling 

5. Machine Learning

6. Artificial Intelligence

7. Robotic Process Automation

8. Deep Learning

Closing Thoughts 

It often assumed that advanced analytics gives competitive advantage. While this statement is TRUE, the foundation of business analytics is in data management and basic analytics - without which advanced analytics will not provide the desired competitive advantages.

One must also note that the level of complexity, costs, and efforts increase exponentially as one moves to advanced analytics. New investments are needed in terms of newer IT infrastructure, software tools, people skills and talent. So companies must be ready to invest to get competitive advantage.

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