Deriving value from big data

Big data: it’s a revolution and applications are endless

An explosive growth in data and information, coupled with advances in technology and a boost in methods of communication, have given rise to an empowered and aware global customer. And to provide legendary experience to their customers, maintain a competitive edge and to make better and sound decisions, organisations need to harness insights from this ever growing “big data”.

Why is it called “big”? Because of five V-s:

• huge in volume;
• highly complex  – value;
• variable – veracity;
• diverse – variety;
• constantly increasing – velocity.

Big data is being generated by everything around us and at all times. Every digital process, social media exchange, sensors, mobile devices and web are generating and transmitting data.

It is estimated that by year 2020, we will have 6.1 billion smartphones globally, our accumulated digital universe will increase from 4.4 trillion gigabytes to 44 trillion gigabytes.

At least third of all this data will pass through the cloud, and the open source software market will grow by 58%, surpassing $1 billion.

Organisations that invest in and leverage big data are anticipated to increase their operating margins by 60%.

Banks need to prepare themselves for the future. They need to analyse the continuously growing data, respond to the changing requirements in real time, learn patterns and predict outcomes, provide security and confidentiality to their customers, grow profitability, engage customers by providing targeted customer advice, protect and enhance their brand and most importantly add value to its customers.

The industry sits on a huge amount of data, which continues to grow every second. Banks can turn this raw data into relevant information like trends, predictions and projections with unprecedented accuracy. Adopting big data analytics can help the banks address some major challenges:

• Profit and margins

Big data insights can help the banks generate higher profit margins by identifying the services that customers want, creating price points for new services and customising services to drive new customer demands by driving offers that matters to individual customers rather than generic approaches.

• Customer acquisition costs

A McKinsey Global Institute report found that marketing consumes 15% of costs for banks. Using big data insights, banks can target the right customer universe and design relevant acquisition campaigns. For example by correlating customer purchase history, customer profile data, and customer behavior on public social media, banks can understand areas of interest and preferences.

• Customer attrition

Big data insights can help banks understand customer activities to predict potential churn or attrition. Some of the indicators pointing to attrition could be cancellation of pre-authorized payments, customer complaints, social media sentiment, and major withdrawals.

• Marketing

Big data insights can add objectivity to business decisions by delivering a consistent and complete customer view across products, channels and systems. Banks can understand how and where a customer fits in the product lifecycle by reviewing all past, present and predicting future behavior to design effective marketing campaigns.

• Risk management

Predictive power of risk models can be enhanced using big data insights by providing real-time risk intelligence and enabling evidence based decision making. For example, banks can segment customers in risk profiles by using information on credit report, spending habit, social media profile, debt repayments etc. and can further use this information to price customers on their credit products, generate preapproved offers etc.

• Fraud detection

With growth in the digital realm, the banks have to face a huge challenge of fraud and scams. Machine learning uses algorithms to detect changes in digital networks by identifying strange spending habits, finding customer anomalies etc. This can help the banks prevent cyberattacks, improve regulatory compliance, detect criminal behavior, and detect credit card fraud.

• Anti-money laundering (AML)

A report by Thomson Reuters states that Standard Chartered was fined $340 million for AML failings. The report also noted that a parallel enforcement action against Deloitte led to a $10 million fine and a one-year ban on all consulting work.

By harnessing the power of Hadoop, an open source big data technology, banks can move their data from a static pool in warehouses to a scenario where data is fluid and actionable in real time. This can enable efficient ingestion, enrichment, analysis, and visualisation of large, diverse and constantly changing data.

• Reputational risk

Banks can no longer solely rely on their experience to save their brand. Big data insights can be used to monitor the web to understand customer sentiment towards the bank’s products, employees, board members etc.

Amazon is a great example of how a company can use big data insights to build its brand, where it realised the emerging trend of online shopping and invested in big data analytics. this enables Amazon to collect a lot of data about website visitors, making it easier to target potential customers with future opportunities.

• Compliance

Big data can help identify trading misconduct by correlating unstructured content such as IM chats, emails, and telephone calls with trading activity. This can help protect the bank from regulatory issues by monitoring illicit trading activity.

• Data warehouse costs

An enterprise data warehouse (EDW) is critical in generating operation reports for banks. But as the volume, variety, veracity and velocity of data increases, traditional systems can no longer run with efficiency. Big data technology overcomes this challenge by enabling the system to scale up to any volume and store, combine, integrate and analyse all data types to generate insights.

The banking industry needs a well-defined approach that starts with identifying big data opportunities, developing and establishing a big data organisation structure, determining costs and benefits and aligning strategic support. Once this is determined, the banks need to focus on building an on-going framework of running big data pilots, assessing business value and continually incorporating improvements to the big data capability within the bank.

Big data is a revolution and its applications are endless. Big data  can bring better profits, better customer experience and better brand value.

By Sim Kaur Somal