Data Science in the Banking and Finance Industry: The Case for AI

Understanding complex and interdependent financial systems has been one of the most tasking pursuits in human endeavor: the sheer volume of data involved, the administrative burden of maintaining and processing historical records, the high resources needed to exploit and make sense of that ever-growing lake of past data… all have conspired to make the financial sector arguably the most obvious beneficiary of data science consulting and the new revolution in machine learning.

Over the last decade, as GPU-driven machine learning systems and cloud-based processing power have reignited business engagement with these technologies, this most cautious of sectors has begun to adopt automation and AI-powered insights across a broad swathe of its portfolio of activities.

In this article, we'll examine some specific fields where data-fueled AI is making a difference in the banking and finance sectors, as well as taking a look at the nature of the challenges involved.

For obvious reasons, black-box AI is problematic for critical deployments in the finance sector. There is industry concern around explainability, model validation, and model drift⁸, where long-term fluctuations in data can make a machine learning model unstable.

When COVID-19 began to significantly affect the economy in early 2020, a number of high-level commercial and finance-related machine learning systems found their definitions of 'spike', 'surge' and 'decline' so radically redefined that it was necessary in many cases to manually intervene⁹ and account for the extraordinary new trends in the data.

Achieving 'elasticity without eccentricity' is a notable challenge for AI applications across the financial sector. Sometimes, as with COVID-19, the data skews so wildly from mission parameters that the model's accuracy can become severely compromised.

To further complicate the challenge, even a highly adaptable model may not always pick the best reaction to 'unexpected' data, depending on its design, scope and variables.

For example, consider the hypothetical case of a 54-year-old surgeon who, having read that playing videogames is helpful to maintaining dexterity for surgeons¹⁰, buys a games console and attempts to purchase a downloadable game from an online store via the console.

This transaction stands out in stark relief from the surgeon's customary spending patterns and far outside her demographic profile. Therefore, the bank immediately freezes her card and requires further authentication that the transaction is genuine, perhaps via a live phone call or a smartphone-based authentication system.

After this, the surgeon downloads her game, and all is well. In future, she can probably buy more videogames from the online store without further interruptions.

But in what ways might a machine learning system interpret this event, in terms of protecting the surgeon in future?

• Singular anomaly

In the most cautious scenario, the model could add 'Buys PlayStation Games Online' to its understanding of the client's spending behavior. This may trigger additional freezes if the surgeon buys a game on any other platform, further impeding the customer experience. It's a granular solution that covers the incident but doesn't advance the model's flexibility, insight or autonomy.

• Minor demographic extension

Alternately, the AI might characterize the customer as more generally interested in video games and permit a slightly wider range of related purchases. Though this increases the attack area, it's a small increase and an acceptable and informed compromise between customer experience and customer security.

• Large demographic extension

What happens if the model is comparing the surgeon to typical histories within her age/status demographic? Having seen a number of people across a historical customer base pay off their mortgages, empty their nests, and indulge in a little mid-life atavism, the AI may begin to expect further such 'indulgent' purchases that are more strongly associated with a younger age-group and socio-economic status. This could make the AI more permissive in terms of allowing deviation from previous behavior, for instance with purchases traditionally associated with a younger age group. In such an eventuality, the attack surface is notably increased.

• Baseline becomes renormalized

In the case of a very poorly-configured model, this unexpected incursion from the 18-24 demographic into the 50-64 age range could cause a reassessment of the baseline expectations of the model, which will now flag purchases by the customer that are typical of her demographic.

• Anomalies become acceptable

By contrast, a less sophisticated model could deduce that the customer has become 'unpredictable', and begin to 'expect the unexpected' from her — probably the most disastrous result in terms of protecting her in the future.

Since these are all technically valid approaches to automated model revision, and since many other types of feedback-driven risk assessment models are susceptible to this kind of rogue logic, it's not difficult to understand the current cautious attitude to model validation and explainability in AI deployments for financial services.

Financial AI systems must make use of three tiers of data, each with diminishing levels of accountability and trust.

• Low risk: structured data

This is likely to be first-party data developed by the company itself, or from actuarial and governmental sources. Such data is not always directly or ubiquitously accessible to the public (for instance, credit scores and information that the government may make available to privileged commercial sectors such as insurance and credit-scoring companies). This type of data will usually enter a machine learning process fully tagged and classified, with little human intervention and with the highest possible trust score. Such a data pipeline is closer to 'automation' of previous analogue methodologies rather than the 'intelligent autonomy' that has dominated headlines in recent years.

• Medium risk: semi-structured data

Here the data is gathered from external systems which have some structure (e.g., HTML, JSON) but lack the internal accountability of the first-party and privileged sources for structured data. The need for greater oversight and verification offsets the advantages of automation to a certain extent. Data in this tier could be scraped from websites or interpreted from documents that already exist as text (rather than requiring OCR). Sources will vary constantly in quality, and any document object model (DOM) on which the machine learning system might rely is subject to arbitrary change.

• High risk: unstructured data

Here the data is abstract and unstructured, such as a web-found image of an individual involved in risk assessment, wherein an external machine learning process such as facial recognition imposes meaning and classification onto a mass of pixels; or image-based text that must be transcribed through OCR and interpreted through Natural Language Processing (NLP). Though this ability to extract hidden relationships from abstracted data represents the most exciting potential of machine learning, in the case of critical financial applications it requires the highest level of oversight and verification by humans, indicating the limited potential for real-time AI-driven processes — at least for now.

The Bank of England report cited above asserts that structured data is used in more than 80% of machine learning use cases, while semi-structured and unstructured data are employed as secondary resources in two thirds of cases.