Data Science in Banking and Finance
The thought of waiting for hours in queues to transact with a teller, sitting with a bank representative who recently joined and has no idea about a particular customer, what his requirements are or what is the best way to serve a customer- leaves customers unsatisfied and disappointed with the financial services of a bank. However, advancements in technology and some highly advanced disciplines like Data Science have the potential to put an end to poor experiences with bank branches.
“Data has been called the lifeblood of the financial sector”- said Jason Moss, the co-founder of Metis
When people in financial sectors discuss about the banks of the future –they generally refer to external things that banks will have in the future. Highly advanced equipments like sensors and touch screens inside the office furniture that are connected to the IoT, banks will have an appealing and attractive appearance to the customers, etc. However, the reality to the banks of future depends on what will happen internally – the decision making processes in financial institutions will become completely data driven with the extensive adoption of data science discipline in finance.
Imagine a future scenario- You walk into a local branch of a bank as your mortgage payments and paycheck is not synced up well. You are worried that your check for your biggest investment i.e. the house might bounce. As you enter the bank branch, the beacons installed in the bank sense the signal that come from your smartphone which is enabled to connect through a token code (something similar to what retailers send when you make a debit or credit card purchase). This token code will help your bank identify that it’s you who has walked in. You are queued into the teller system by the bank’s intelligent systems and the moment you approach the teller counter – the bank representative has all the information about you readily available. The bank’s intelligent system developed by the data scientists provide the bank representative with information related to all your accounts, your balances, your tenure of relationship with the bank, what banking products or services can the bank representative offer you, etc.
“Know thy Client” is the success mantra that financial firms have sworn by forever and data science is the secret to achieve this success mantra.
Why financial institutions need Data Scientists?
A recent news article on Business Insider highlighted –“JPMorgan is hiring data scientists”. The giant bank has posted requirements for hiring data scientists who can take over the overall development of data models that use pattern detection algorithms in electronic communication.
Goldman Sachs bank also posted openings for data scientists who can identify any fraudulent or risky behaviour in the data that their existing surveillance framework does not.
Financial institutions including JPMorgan, Citi Bank, Goldman Sachs, HSBC, Deutsche Bank are hiring more data scientists in 2015 to enhance their services to clients – and you don’t have to be working a bank to clinch one of these data scientist jobs.
CLICK HERE to get the 2016 data scientist salary report delivered to your inbox!
“Recruitment for data scientists remains in “its infancy” within the banking sector. Some banks are reacting more quickly than others, but some still don’t appear to have a strategy to manage Big Data in place, whilst others have a clearly defined plan of action which they are engaging with. Banks typically do not react as quickly as other industries. They are like oil tankers, but then all of a sudden there will be a burst and demand will kick in.”-said Robert Grant, Sales Director, at banking technology recruiters Cititec.
With all organizations focusing on big data, it is difficult for financial institutions to find data scientists who can crunch, organize and make sense of petabytes or zettabytes of banking data. There are only few universities that offer graduate programs in data science making it difficult for the orthodox financial firms to find data scientists with a graduate degree in this discipline.
Financial Institutions are investing in big data technologies for quite some time with technology leaders banking on the fact that analytics can prove to be helpful for varying needs in future like- improving trading strategies, portfolio management, regulatory reporting and client targeting. Unless, financial institutions want their big data investments to be in vain, it is extremely important for them to hire data scientists who are adept in statistics to make use of big data tools and technologies effectively.
Banking and Finance industry is bogged down with several challenges like- competitive environment, emergence of novel communication channels, demanding regulatory guidelines, rapidly changing customer requirements and consumer environment. With all these challenges on the forefront, financial institutions need an opportunity that will help them stay ahead. Effective and accurate data science methodologies will differentiate the financial leaders from the followers.
Financial institutions have always had big data but now these financial institutions are focusing on building large teams of data scientists that can help them gain advantage over their competitors. The more a financial institution knows, the better it can predict its customers’ requirements and wants. The major decisions in financial institutions like controlling costs, identifying risks and growing revenue are driven by the popular discipline of data science. Data science is the secret to success for gaining data-rich-insights providing financial institutions with opportunities to cross-sell, upsurge business results on a systematic basis and delight customers.
According to Infosys and FICO, banks will increasingly depend on data scientists for regulatory compliance, operational efficiency and fraud detection. Data scientists are the experts who have the broad skill set to help banks attain knowledge from data and finally gain wisdom from knowledge. If you are planning to develop your career in this field then bringing in more wisdom into the world of data science through professional training is not a bad thought.
How data science helps banks?
Gartner report titled “2014 CIO Agenda: A Banking/Investment Perspective” revealed that utilizing and investing in analytics and business intelligence is a top technology spending priority in 2014 for CIO’s.
Most of the large US based financial institutions are using data science algorithms to understand customers in a better way by analysing different channels customers use - such as ATM’s, offline bank branches, mobile app, call centres, online banking, etc. Data science helps bank in several ways-
- With several new channels, banks can serve their customers with their preferred channel by leveraging transactional behaviour analytics through various data science algorithms. This also helps financial institutions identify usage for particular products and transaction patterns across various customer segments.
- Data science can add great value to financial institutions by helping them find attributes and patterns which have increased probability for fraud.
- Data science helps banks optimize the check float criterion by considerably reducing the bottom line costs. All the transaction records go through a decision management process where various Behavioural Scoring Techniques are applied to find out whether to float a check and for how long.
- Data science helps financial firms ensure customer satisfaction on quality of service through data-rich insights on changing customer requirements and satisfaction levels. Data science helps banks build a 360 view of its customer by aggregating cross-lob and external third party data that helps financial institutions serve their customer in their preferred way.
- Data science helps financial institutions forecast various profitability components such as charge-off accounts, delinquency and closure that help them make effective product and pricing decisions.
Data Science – The Heartbeat of various Financial Firms
- Data science is at the core of Capital One. Capital One Labs – a tech startup, with Capital One is making an impact on nearly 60 million customers by using data science algorithms to develop next generation of financial products and services. Analytics is highly influential at Capital One bank as the bank has significantly grown its earnings per share by more than 20%.Capital One is now the third largest provider of credit cards in US.
- Citi Banks - Citi Latin America Innovation Lab is home to a group of data scientists that experiment with innovative ways of offering its commercial customers with transactional data combined from its global customer base that helps clients identify novel trade patterns.
- Bank of America runs BankAmeriDeals with various cashback offers for debit and credit card holders based on the analytics of the payments made by customers in the past.
- Credit Suisse has petabytes of structured data about various financial transactions that it stores across various continuums that consist of bulk storage, RDBMS, in-memory databases, analytics operations. Data scientists at Credit Suisse find out novel opportunities to create revenue streams; retain customers and reduce expenses.
“Finding Data Scientists is hard enough, but to find somebody with data science skills and experience in the financial services field is near impossible.”- said Nigel Faulkner, CIO of Credit Suisse investment bank
You can be the Winner!
Financial Institutions have not yet effectively used data science to generate actionable insights but, with increasing competitions, banks are now realizing the importance of data science and acting on it to hire talented data scientists. Data science discipline in financial services industry will soon revolutionize the relationship between the customers and banking firms in innovative ways that are unimaginable now.
Who gets hired as a data scientist depends on specific requirements of a bank – whether they value practical experience and domain expertise more or they are looking for an enterprise data scientist with the broad skill set. People who are a mix and match of the two will definitely attract more employers. Data science in financial services industry is actively evolving with great opportunities from all fronts and is expected to grow over the next 5 years.
This is the right time for a professionals to strike the iron while it’s hot and etch out a career in Data Science in the finance industry. Think you have got what it takes to become a great enterprise data scientist? Learn about various Data Science Courses to show the banking executives that unicorns do exist in reality.