Unlocking the digital potential of financial services through predictive analytics
Data has always been at the heart of the financial services sector. From overcoming regulatory challenges to helping customers set up a bank account, data has been the silent engine that has powered the industry.
As such, it is surprising that the industry has hesitated when it comes to implementing innovative data technologies. This can be partly explained by strict regulatory standards that control the industry, which may stunt digital innovation due to challenges tied to compliance.
Yet, what cannot be ignored is that industry leaders championing data transformation on the frontlines are doing better numbers and business.
Predictive analytics is the master key that can unlock the digital potential of the financial services industry. Stubborn issues of trust and regulation can be exacerbated by the overall weakness of companies’ data pipelines – sinking the value of the data at their fingertips.
So, what can data and IT leaders do to overcome these challenges? Let’s dive in.
You’ve got to have faith
Trust is the bedrock of the service industry, and financial services are no different. Everyone from the customer through to the data analysts must trust the decisions being made.
Yet, with advanced machine learning reducing humans to a rubber-stamping position, genuine trust can be difficult to build. This puts a great deal of importance on the way the algorithms behind predictive analytics are made and the quality of data that drives them.
According to recent research, only half (50%) of IT leaders trust that the decisions being made by predictive analytics are without bias. More shockingly, less than half (45%) trust these decisions to be wholly accurate.
So, financial organisations must prove to their customers that they can make reliable decisions and can firmly trust the data at their disposal. A key part of this is ensuring that the data going
Financial services firms operate in a highly regulated environment, with $270 billion per year spent on compliance and regulation, the equivalent of 10% of operating cost.
This puts a great deal of pressure on IT analysts to cautiously comply with regulations in a way that may limit digital innovation, leaving it lagging behind other industries.
When interviewed, 39% of IT leaders said that complying with regulatory standards would prevent them from introducing predictive modelling or predictive analytics solutions in their organisations.
Despite these fears, predictive analytics could actually simplify the confusing world of data regulation by helping employees ensure their use of data complies with regulatory frameworks.
This may include actively helping IT teams manage their data retention policies or assessing when to dispose of personal data in a timely and safe manner. IT leaders appear to recognise this opportunity, with nearly three quarters (72%) believing the use of predictive analytics in business intelligence platforms can help financial services organisations comply with data regulatory frameworks.
Strengthening the analytics data pipeline
An organisation’s data pipeline is the pillar that determines the success of predictive analytics. In short, it is the conduit that takes raw data and turns it into analytical-ready information, then makes it continuously available to the rest of the business at scale in secure and governed ways.
A robust analytics data pipeline enables organisations to go beyond making decisions with data. When working with real-time, hyper-contextual data, the business can be empowered to take informed action in the business moment, even more so if they utilise alerts and automated responses which are triggered by changes as and when they occur in the data.
As such, platforms must be informed by up-to-date and complete data to build trust in the actions taken using predictive forecasts. Currently, that appears not to be the case.
Many IT leaders in financial services organisations reported challenges at the start of their data pipeline when initially integrating the data that then feeds their predictive analytics programmes, citing concerns over its quality (40%), privacy issues (30%) and the speed of the integration process (36%). IT teams must work collaboratively to identify potential cracks where value is lost to unlock the full potential of predictive analytics successfully.
Integrating human and machine intelligence
Undercutting much of the intelligent decisions that drive financial services organisations is a crucial element that cannot be forgotten: humans. Humans can provide oversight into these decisions, and more importantly, can explain those decisions to the customer.
To avoid making a customer feel like decisions were being made about them that couldn’t be explained, organisations need to be transparent in their decision-making processes internally and externally. A human has to be able to explain the decisions.
Blending human and machine insights improves the accountability and explainability of actions being made, which helps smoothen some of the hurdles around trust and regulation. This comes as no surprise, with more than two thirds (69%) of IT leaders in financial services advocate incorporating predictive analytics into business intelligence platforms.
Marrying predictive analytics with business intelligence will unlock the potential for digital transformation in the financial services industry. Integrating the best of human and machine intelligence allows organisations to be better prepared to make more informed, accurate and trusted actions in the business moment – driving bigger and better results for their customers and business.