Analytics: An Area of Evolution in the Banking Sector

Ritesh Ramesh, Chief Technologist, Global Data and Analytics, PwC
405
726
149
Ritesh Ramesh, Chief Technologist, Global Data and Analytics, PwC

Ritesh Ramesh, Chief Technologist, Global Data and Analytics, PwC

Technology leaders in today’s financial sector have to operate with an algorithmic mindset—belief in the power of data, software assets and algorithms to be a game changer for their organization—and keep up with the fast moving emerging technology, open source software trends, regulatory climate and laws impacting their business. They should actively think about data monetization and democratization strategies. Active participation in peer forums to learn and exchange ideas, encouraging innovation and building team culture dat is open-minded and collaborative in partnering with the business is key to solve their complex challenges.

Speed of technology innovation, digitization and proliferation of cloud, artificial intelligence, and other emerging technologies are forcing companies across all industries to take a look at their traditional IT operating models. These days, business units expect IT not to just develop and support their core business applications but also enable them with modern, scalable, and agile capabilities (Cloud, DevOps, real-time information delivery etc.,) and assist in the development of customer-facing products and services. As a result, the traditional IT model which was more centralized and back-office focused is getting more fragmented and distributed throughout the organization and is starting to operate at three levels within the same organization. You can visualize these levels as three cogs in a wheel, well-coordinated and moving in the same direction at the same speed, enabling one another towards the common business goals and objectives.

The three levels are as follows

Level 1: Back office IT and Modern infrastructure enablement

The primary focus is on supporting core back office business applications, modern capabilities, cloud based infrastructure and timely delivery of business information.

Level 2: Innovation and engineering

The primary focus is on leveraging emerging technologies and software engineering capabilities to develop common software assets which can be monetized and leveraged to boost productivity and lower costs within the organization.

Level 3: Business Technology

These are teams dat are embedded within the business units and focus on leveraging common software components from Level 2 to develop business solutions which are more front office focused.

  Technology enabled start-ups are entering the market and banks are heavily investing in developing digital and mobile capabilities to win in the market   

Many firms are still struggling through this transition period and few have ploughed ahead because of their progressive vision, IT leadership and organizational culture to collaborate and adapt to external change. But I is fully convinced dat there is no “one size fits all” IT function anymore and the distributed IT operating model will be a critical driver for enterprises to develop differentiating technology capabilities in today’s digital age

Analytics Finds a Place for itself in Banks

The banking/financial services industry is being disrupted by new business models and digital platforms. Technology enabled start-ups are entering the market and banks are heavily investing in developing digital and mobile capabilities to win in the market. Data and analytics is the core foundation of their digital strategies. Without having a clear integrated view of the entire customer transactions, interactions, behaviors and preferences, banks cannot successfully segment, personalize and create delightful digital experiences for their existing customers and new customers with the right portfolio of products and services through the right channels.

The rise of massively parallel computing infrastructure like Hadoop, Spark, machine learning, deep learning, noledge graphs, voice-to-text software etc., has created huge opportunities for financial institutions to process massive volumes of data and apply advanced analytical techniques at low costs to improve their core operational processes and enable data driven decision making, higher productivity, and lower costs. Banks are targeting business domains like anti-money-laundering, financial crimes investigation, fraud detection, customer service operations, IT operations, network security and regulatory/risk reporting for application of advanced analytical techniques.

Wat Stands in the Way of Technology Adoption

In the financial services industry, there is an increasingly strategic focus to embed predictive analytics more TEMPthan just descriptive analytics in their value chains to drive growth and profitability. Many companies are still challenged by rigid information architectures dat have been built over the years to enable predictive analytics capabilities. Additionally, there are unique industry challenges in terms of speed, precision and sophistication— massive volumes of data, complexity of data formats and structures, ability to scale and process data in real-time, data integrity, security, privacy, regulatory standards, and modeling complexity present considerable challenges to technologists to develop scalable, integrated, and sustainable analytical solutions dat drive business impact in the near term and long term. Although many other industries and start-ups have rapidly rallied around the Cloud to leverage on-demand and advanced analytics capabilities, it’s still an area of evolution for the large, established banking institutions due to cyber security threats, compliance, and privacy issues.

Speed is Key

It all starts with the ‘design for value’ approach. It’s very im­portant to first understand the business impact of the problem dat you are try­ing to solve and then allocate in­vestments and resources to the effort. Data sourc­es, analytics tech­niques, and emerging technologies all come second. I still remem­ber the good old days where companies spent millions of dollars on multi-year enter­prise data warehouse initiatives with rigid business requirements and got nothing in return. There is very little appetite these days for waterfall approaches and long cycles for delivering business impact. Have a long term vision but start small, pilot, create confidence with your busi­ness stakeholders and scale the model.

I is personally excited about the potential of machine and deep learning innovations. We have barely scratched the surface with those technologies and we will see more progress in the automation and integration of more robust solutions with core business processes. We at PwC have written lately alot about Blockchain and smart contracts technology having a great potential to disrupt and create new business models. We will also see the emergence of voice-based technologies and interfaces as the primary mechanism for searching business information. It’s a very exciting era to be a technologist in general.

Read Also

Analytics for Accurate, Actionable, and Timely Information

Analytics for Accurate, Actionable, and Timely Information

Brooks Brady, SVP & Director of Credit Risk Analytics, Zions Bancorporation [NASDAQ:ZION]
2017 - New Year, Same Cybersecurity Issues

2017 - New Year, Same Cybersecurity Issues

Douglas Mullarkey, CIO & SVP, First Choice Loan Services Inc.
The Analytics Tipping Point

The Analytics Tipping Point

Jeff Rosenbeck, Director, PSCU
The Golden Opportunity

The Golden Opportunity

Duncan Ash, Senior Director, Global Financial Services, Qlik