There is no shortage of data available for banks.
In fact, companies like Accenture and Oracle have created new divisions dedicated to creating and managing data for financial institutions.
However, banking institutions have a hard time using and accessing this data, primarily due to regulatory and internal organizational constraints that make the leveraging of new, advanced tools such as machine learning a daunting task.
That said, here are the best practices and techniques that banking institutions can utilize to leverage their data for machine learning.
1. Standardize Data
The main problem banking institutions face nowadays is the accumulation of data in different systems, which makes the idea of a ‘universal’ client record nothing short of a joke.
In this case, bank consolidation is among the main culprits, even though under-investment and organization structure in back-office systems in the past 8 years has had a considerable effect.
To deal with this, banking institutions ought to begin from a small set of data before building up to develop a universal client record as time goes on.
The process of managing data can be sub-divided into three categories – Data Modeling, Database Management System, and ETL (Extract, Transform, Load).
Every step calls for specialized tools, including Informatica, Teradata, and SQL Server, even though some new entities such as Trifacta and Paxata support the normalizing of data primarily for machine learning tools.
2. Use Off-the-shelf Machine Learning Tools
Currently, each big data company is in competition to lure developers into using their marketplaces and machine learning tools, which is good news for banking institutions trying to venture into the space as costs are not only negligible but also consist of a reasonable share of functionality to help them start.
The data science tools developed by Amazon, Google, IBM, and Microsoft are all very similar, and thus it comes down to data security policies and the use case of the individual banking institutions.
Some new startups are currently developing tools to tackle discrete machine learning activities and tools to assist in data analysis, including Tableau, Olik, and Microstrategy.
It is advisable that banking institutions begin with these off-the-shelf solutions to help in delivering several quick wins in a bid to obtain institutional backing for each new program.
Upon getting buy-in, there’s a lot of scope for future initiatives that involve shifting towards unsupervised learning and creating machine learning algorithms.
3. Testing and Learning
At this particular stage, the data has already started generating results.
As such, A/B testing is necessary to not only assess accuracy but also acquire a high level of confidence.
For instance, if the data is utilized in extrapolating trends and starting to forecast behaviors, a data scientist ought to begin validating all the variables, particularly in the regression analysis and testing the forecasted results against an already known data set.
4. First Start with an Internal Product
Identifying the machine learning product’s initial use case for banks can be a difficult task.
There has been an increased interest in bots to assist with customer-facing activities.
However, there are various problems surrounding consumer adoption and behavior, which might drag the practical advantages.
For instance, Tay, a machine learning-driven bot from Microsoft that was responding to chats and tweets was switched off because of its inability to identify when it was making racist or offensive statements.
Beginning with an internal-based project for a given bank is undeniably an excellent technique.
There are numerous projects that could have quick revenue with minimal risk, including customer acquisition and churn analysis.
Irrespective of whether banks want to begin with an external or internal project, the changing pace calls for them to begin thinking more about unlocking their data’s value.
In a survey involving 424 senior executives drawn from both fintech companies and financial institutions, Euromoney discovered that most of the respondents were convinced that portfolio management, financial analysis, and risk assessment roles would be affected first by the rolling out of machine learning / artificial intelligence in the coming three years.
Nonetheless, such forecasts are presently becoming a reality, with banking institutions such as Goldman Sachs witnessing a decline in revenue in various fields including stock trading and equity underwriting, and new tools that leverage machine learning such as Robin Hood that provides free stock trading experiencing increased usage.