Big data is no longer an industry buzz word – we are very much at the ‘end of the beginning’ for big data, and that introduces real uncertainty, and opportunity. This is particularly true for banking and financial institutions, which are coming under severe pressure to shift focus from products to customers.
Understanding customer behavior as well as social and market trends will be key to the ability of financial institutions to retain customers and grow market share. Today, financial service organizations are leveraging big data analytics for strategic and competitive gain, to help transform processes and operations and –to identify new business opportunities.
Roughly three quarters of organizations that haven’t already deployed big data solutions appear to either have pilot schemes in place or are well in to the planning process- One of the main challenges facing new entrants is the lack of publically available use cases and reference architectures; those organizations that have successfully invested in big data to optimize their workflows may keep details closely guarded for the time being to maintain a competitive advantage.
Think about a banking network with millions of customers, each with a different activity profile and set of ‘normal’ or expected actions. This brings into focus a number of complex variables that need to be weighted, classified and correlated.
Using big data analytics, banks can harness all the historical data to model customer preferences. The results can then be used to personalize event based marketing campaigns for new products and services. When coupled with a coordinated messaging across email, mobile, branch and ATM interactions, these targeted, personalized marketing campaigns have a much higher probability of conversion than traditional mass email campaigns.
With cashless transactions becoming the norm, fraud is another big issue. Banks needs to continuously monitor client behavior for anything anomalous. This is done by monitoring the time, geolocation, transaction amount, transaction frequency, items purchased and then mapping the behavior against a template of what ‘normal’ looks like for that customer.
Bear in mind that ‘normal’ for December may be very different from ‘normal’ in July. Spatio-temporal problems like this are non-trivial, and solving them requires highly efficient processing at scale. With data streaming in thick and fast and potentially large financial transactions at stake we ideally want to detect anomalies accurately and within a small time window. Accuracy here means not stopping valid transactions (false positives), and not allowing fraudulent transactions (false negatives).
The problem of minimizing false positives and false negatives is a notoriously hard problem in computer science, typically requiring a blend of statistical and computational intelligence techniques and frequent training and tuning. Insights gained from the massive datasets processed by big data, together with new anomaly detection methods employed with big data are likely to really help optimise processes here.
Perhaps the biggest challenge introduced by big data is the need to re-evaluate the storage-compute model.
The biggest benefits of big data are reaped by organizations that have a lot of legacy data. This could require moving historical data, integrating with that data, or un-archiving that data from long term storage. This has implications for traffic management, security, data handling, and storage.
Financial institutions may be reluctant to move sensitive data off premise, Many organizations cannot afford to build and tear down datacenters to handle their processing and storage scale demands, nor do they have the agility needed to deal with rapidly changing high volume unstructured datasets. Cloud Computing is fast becoming a keystone in our thinking about the way we architect data centers, One can foresee institutions deploying hybrid cloud solutions at both a strategic and tactical level to handle big data tasks, perhaps anonymizing data or covering regulatory concerns through service level and data confidentiality agreements.
Internet of Things (IoT)
We can’t mention big data without also mentioning the Internet of Things (IoT). By 2020 various industry estimates put the number of internet connected devices between 50 and 75 billion. This is going to radically change how humans interact with technology, the visibility we have on the state of these ‘things’, and the insights gained from analytics on those ‘things’.
In practice, this will result in the generation of much higher volumes of unstructured data (through instrumentation, external feeds, etc). All this data will need to be stored in the enterprise data centers and analyzed using big data solutions – something that needs to be considered and factored in to future IT planning.
Big data is relatively new; it has only been a decade since Google published the seminal MapReduce white paper, and as with any new technology the primary concern is functionality. This introduces a number of security challenges, not only in the secure handling and storage of the data, but in understanding the nature of the data itself, and how it can be manipulated to create insight (and potentiality breach confidentiality policy).
At the most basic level, big data components may include only rudimentary access control and integration with systems such as Kerberos, and depending on the components you choose, may introduce additional vulnerabilities when mapped against a mature security framework. It’s also important to determine how long to keep this data and how to ensure that data integrity is maintained (over potentially many years). With big data there may simply be a lot more data, but the scope of it may also be much broader, and it is likely to be more granular as the drive to instrument everything continues. These remain important concerns; especially in the heavily regulated financial services industry. Fortunately there is a considerable effort in mitigating these challenges through conventional security techniques as well as emerging technologies such as block chain.
Seizing the Opportunity
Financial service organizations have no physical product to sell. Data, and the associated workflows are business-critical assets. Big data offers the promise of real differentiation for early adopters, especially where competitive advantage can be opportunistic and short lived. The most effective strategy for big data adoption will be to identify core business requirements, and then leverage existing infrastructure as part of a phased migration, ideally taking a specific project as a proof of concept in order to build up the necessary data science skills, assess deployment, storage and archiving models and address regulatory and security concerns.
Glen Ogden, Regional Sales Director, Middle East at A10 Networks