Future of Banks with Data Science at core

The competition among financial service providers forces them to reduce the cost of their services and at the same time improve the customer satisfaction and convenience. In order to achieve the improved business performance, the banking industry has been relying on Artificial intelligence powered by big data and machine learning aka Data Science.  According to world economic forum this is where financial services are going and what technologies will enable the financial institutions to provide these services.

Figure 1 Forum, W.E. (2016) Future of Financial Services. Available at: Source  (Accessed: 13 June 2016).

What is central to all these sub sectors? All of them need to make use of data analysis in some way or the other.

Day by day new companies are entering the financial market with innovative solutions relying heavily on Data analysis. As discussed in our previous blogs in the series, the biggest threats and challenges these companies pose for the traditional financial industry players are:

•            Sub-sector Boundaries are blurring – The newer products from these innovative entrants are creating competitive pressure across sub-sectors. Phenomenon like alternative lending, crowdfunding and robo-advisors like betterment.com are challenging the retail banks and taking their customers away.
•           Customer ownership is lost – Traditionally, financial institutions relied on bundled services. But the emergence of these niche providers encourages customers to shop around. Moreover, the technology oriented firms offer amazing digital experiences, which is a preferable choice for most specially the youngsters.
•           Customer behaviour is changing – With all these online options the customers have a wider mix of options available to choose from. Young adults are taking control of their finances and want the best experience from their financial services providers at a minimum cost. If you can not provide what they want, they will be happy to switch.

Given these challenges, the traditional financial institutions also have an opportunity. The traditional institutions can also leverage the use of Data science in their operations and use the data from both on-grid as well as off-grid channels such as social media to provide on par services to their customers. The early adopters are already reaping the benefits of big data. As per a recent global CIO survey conducted by CSC (CSC, 2014), 72% respondents from financial services sector said that Big Data had a positive impact on their rate of innovation and 77% believe that Big data has improved their productivity and efficiency. 

The figure below shows the emerging application areas where financial sector now is using data science to provide better services and improve business performance.

Source: Frost & Sullivan, (2016), AI impacting Banking & Financial Services Overview [ONLINE].

Available at source [Accessed 13 June 2016]

These threats and opportunities balance the future landscape of the financial services. It also means that it is unlikely that this landscape will be populated by many outright winners or losers per se. Rather, companies will be collaborating with each other to help themselves and others, especially, in fraud detection and prevention – which is a huge priority for the sector. It is possible that we may be seeing a new platform that aggregates information from entire industry and find trends and patterns in fraud. Thus if a new treat or scam appears all the banks can be informed immediately so that the risk of multiple banks seeing the same frauds will be mitigated immediately and that too without each of them investing separately in the infrastructure to make this happen.

Despite all the positive influence there are challenges that are common to all financial players looking to implement a big data strategy. One of the key challenge for big data is getting quality data, Banks although have wealth of data most of this data is kept in silos within different divisions. Making a central data repository or as some experts say, “Data mart”, is challenging as it requires integration within old legacy systems. This is time consuming and expensive. The other challenge that banks face is that most of the big data strategies have cloud as their infrastructure platform. Financial institutions in particular are hesitant about bringing their data to cloud as the regulators keep a tight check on them. Thus understandably, banks prefer to err on the side of caution. But as the cloud services evolve and get more secured, this can change very soon. One challenge that financial industry luckily does not face is getting the right people to get insights from data. Although, they have the right people to do it, it is expensive for the banks to keep employing them.

Some of the biggest banks are already testing their newer applications on cloud. For example, a consortium of major world banks including JP Morgan Chase, BofA Merrill Lynch, BNY Mellon, BlackRock, Citadel, Citi, Credit Suisse, Deutsche Bank, Goldman Sachs, Jefferies, Maverick, Morgan Stanley, Nomura and Wells Fargo recently invested in a venture called Symphony to provide secure, cloud based communication platform (LLC, 2014).

With all that said, it is evident that Data science is going to be integral to a banks digital strategy for all major innovations and operations. As the DBS bank CEO Piyush Gupta says on his digital strategy, (Tan, 2014) “That is actually going to make the difference between the banks that will survive and the banks that will not survive”.

Bibliography:

Frost & Sullivan (2016) Artificial Intelligence Empowering Digital Banking and Finance Ecosystem-IT, Computing and Communications. Available at: http://cds.frost.com.proxy.library.cmu.edu/p/55399/#!/ppt/c?id=D881-00-03-00-00&hq=data%20science%20finance (Accessed: 13 June 2016). In-line Citation:(2016)

CSC (2014) Big data - CSC global CIO survey: 2014–2015. Available at: http://www.csc.com/cio_survey_2014_2015/aut/115261-big_data_csc_global_cio_survey_2014_2015 (Accessed: 13 June 2016).In-line Citation:(CSC, 2014)

World Economic Forum (2015) Future of Financial Services. Available at: http://reports.weforum.org/future-of-financial-services-2015/executive-summary/?doing_wp_cron=1465787851.6013619899749755859375 (Accessed: 13 June 2016).              In-line Citation:(2015)

LLC, S.C.S. (2014) Press release. Available at: https://symphony.com/press_releases/item/consortium-leading-financial-firms-invest-new-communication-workflow-platform (Accessed: 13 June 2016).In-line Citation:(LLC, 2014)

Tan, J. (2014) Piyush Gupta demands A shift to digital banking in Singapore. Available at: http://www.forbes.com/sites/forbesasia/2014/06/04/piyush-gupta-wants-a-shift-to-digital-banking-in-singapore/#728fe5f14d09 (Accessed: 13 June 2016).In-line Citation:(Tan, 2014)

Data Science in Finance

sIn our first blog, we introduced the buzz word - Big Data and briefly mentioned about how banks and insurance companies are making use of Big data or Data Science. In this blog we will mention how some of these organizations are leveraging the capabilities of Big Data. To begin with, I would say that the financial sector can be broadly classified into the following categories: banks, investment funds, insurance companies and real estate or Asset Management(Investopedia.com, 2007). Traditionally, these businesses can serve either a retail customer or a commercial customer or both.

As in the financial sector, which operates in terms of transactions, covering risk is the most important aspect of their business. Data can help these companies in minimizing the risk. It is interesting to see some of the top aspects in which Big Data has changed the way finance industry operates. Although they all are linked up to some extent, I will still categorize them separately to mark some dist

          

1. Transparency : Historically, the big financial transactions were based on relationships. People used to do business with the people they trusted. As the data about financial market was made public, investors were able to identify the potential risks of working with particular organizations. As more and more data is added to the arsenal, it becomes difficult for the fraudulent companies to cover up. It also leads to sound investment decisions and these can be made based on company performance.
2. Risk Analysis : Lenders can now approve credit applications on the spot. This is enabled by big data. Banks are able to create a risk profile of a customer by checking customer credit reports, spending habits, credit card repayment history. Another example would be to prevent fraudulent transactions on credit cards. Based on a user past transactions, banks can identify if the on coming transaction may be a fraudulent transaction and additional security can be added for such transactions.
3. Fin-tech or Algorithm based trading : Real time stock market information, news feeds, and social media generate a lot of unstructured data. This data can be fed to a machine learning algorithm to predict the market movement. This helps investors in making safer investments while trading in the stock market. People are writing algorithmic codes to trade automatically as the market moves. The thinking part is done by the algorithms. One of the articles from the wall street journal published recently states that “computerized trading strategies, or algorithms, are remaking the $12.7 trillion Treasury market, emulating earlier sea changes in stock and currency trading” (Burne, 2015).
4. Consumer Analytics : Financial companies who had been keeping their data in silos, were for long worried about the challenges of integrating the data and process this humungous amount of data and the costs associated with it. With the onset of Big data technologies, they now are able to leverage the benefits of insights provided by this data and use it to know their customers much better. This not only helps in keeping their customers loyal but also helps in tapping on the customers of their competitors if they are missing on this advantage.

Some of the challenges that these companies are trying to address are mentioned below:

·       Integrating large volumes of structured data with unstructured market data.

·       Real time analysis of counterparty exposures and unstructured markets to evaluate market risk.

·       Dynamic economic environments, ongoing regulatory changes declining economies and upcoming trends require improvement to IT systems which require further investments.

Big Data technologies when used, can help address these challenges and provide value to the companies. The table below summarises how can the financial services industry use big data

Fig 1 How companies in Finance Sector can use big data

Bibliography

Investopedia.com (2007) ‘Financial sector’, in Available at: http://www.investopedia.com/terms/f/financial_sector.asp?layout=infini&v=5C&adtest=5C&ato=3000 (Accessed: 29 May 2016).In-line Citation:(Investopedia.com, 2007)

Razin, E. (2015) Big buzz about big data: 5 ways big data is changing finance. Available at: http://www.forbes.com/sites/elyrazin/2015/12/03/big-buzz-about-big-data-5-ways-big-data-is-changing-finance/3/#68cf1435a482 (Accessed: 29 May 2016).

In-line Citation:(Razin, 2015)

Burne, K. (2015) The new bond market: Algorithms trump humans. Available at: http://www.wsj.com/articles/the-new-bond-market-algorithms-trump-humans-1443051304 (Accessed: 29 May 2016).

In-line Citation:(Burne, 2015)

Bibliography: www.pwc.com (no date) How can financial services sector unlock the value? Available at: https://www.pwc.com/us/en/financial-services/publications/viewpoints/assets/pwc-unlocking-big-data-value.pdf (Accessed: 29 May 2016). In-line Citation: (no date)