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Big Data and Trend Spotting with New Customers

Big Data and Trend Spotting with New Customers

Big Data has the potential to revolutionise how financial institutions conduct their KYC (‘Know Your Client’) due diligence.

Internal data collation is not serving most companies, data sets are incomplete and large decentralised systems used by organisations mean that information on a customer can be spread across multiple internal systems. KYC information is often outdated as it was collected when the customer came on board, possibly many years ago. This causes incredible inefficiencies of time and resource to wade through the data, and information provided by this data often leads to poor decision making. The type of information institutions have access to is often a credit report, maybe some past data and anything declared by the client – again, this lack of data leads to difficult decision making. 

The landscape is changing. With new technology and more data, companies can obtain and store data about potential customers they could have only dreamed of ten years ago. 

Internal Data

Financial institutions should ensure that their own internal data is stored centrally so that silos of information don’t spring up in different departments. With modern CRM systems this should be achievable. Once data is centralised and cleaned internally this allows institutions to layer on more information, build deeper knowledge of customers and begin to spot behaviour trends and correlations.

Big Data

The potential to access data from third party providers to get a more complete understanding of the new customer is mouth-watering. Public databases, consumer reporting agencies, other financial institutions and many other avenues are all potential sources of information on new customers.

Collaboration Makes Sense

There is a huge inefficiency in each institution collecting their own information on customers. Big Data offers the possibility of storing data centrally and reducing the burden for everyone who needs it. The debate on customer privacy will be fierce and ongoing but the benefits of greater simplicity and transparency are clear – although for unscrupulous customers the status quo clearly suits them a lot better.

Risk versus Operational Efficiency

On-boarding new customers in the current climate is risky. There is a ton of legislation and it can be horribly inefficient. A lot of the data collection is via self-declaration, and as well as being open to abuse and time consuming can really put off customers - 99% of whom are completely legitimate. So an institution is always weighing up risk versus speed. Big Data is so exciting because it opens up the potential to more efficient data which means institutions can get customers through the process much faster. 

Applications of Big Data

The applications are endless. If a financial institution wants deeper and richer data on potential customers this data is becoming more comprehensive all of the time. From social profiles, to driving data, to health data and budget management tools. All of this data is useful and can be plugged in to a view of a potential new customer and their creditworthiness.

Trend Spotting

With machine learning technology evolving all of the time, making predictions about future behaviour is possible. Big Data could show various correlations between behaviours and likely credit default. Banks could track these trends with their customer base and then evaluate new customers using the same criteria. Theoretically, red flags on credit worthiness could be thrown up for all sorts of reasons what banks don’t have access to right now.

It is hard to predict how Big Data will affect working with new customers. The opportunities are endless, and it will likely be a battle between the banks and the regulators to determine how much information banks can access and use. What is not hard to predict is that institutions who want to learn about new customers will have more information than ever.

How we helped NSL telecoms

Read case study

21%

increase in revenue per client

Read how we helped NSL Telcoms grow revenues and manage risk in its business

How we helped Wave Energy

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12%

decrease in customer default

Read how we helped Wave Energy use data to explore growth opportunities and mitigate risk in its business

How we helped Towergate Insurance

Read case study

40%

increase in sales team performance

Read how we helped Towergate insurance gain control of their data processes and see results across the business

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