Your digital money trail: How AI is changing financial privacy

By Mukesh Pandey, Founder & MD, Rupyaapaisa.com

The digital payment system leaves behind a lot of information. Be it a payment made via a UPI transaction, acquiring something with a credit/debit card, a subscription to an online service or a loan secured through an app, it generates data about our spending behaviour. All of this data is extremely valuable for both banks and technology companies nowadays, as more and more services move online.  AI is good and complicated when it comes to financial privacy. On the one hand, it helps to identify fraudulent transactions and customise services. It can perform all of this in a matter of seconds.

Every payment tells a story
To start analysing financial data, one might find that it is not that bad to make a grocery purchase using UPI or settle a restaurant bill with a card. In case a person makes a few transactions, this might not lead to any issues.  However, on the contrary, if thousands of transactions of an individual are evaluated and analysed, a clear picture of a person’s financial behaviour can emerge. The patterns formed can show how often one spends money, where in particular one prefers to spend it, how often automatic payments are made, and so on. Having combined this data with other digital information available, the patterns observed can be fully comprehended only by AI nowadays.

AI developed financial surveillance systems

The artificial intelligence system can detect any anomalous payment by monitoring several aspects, such as transaction value, place of action, device-related information, timing and specific behaviours in the past. If there is something unusual about the transaction, the service will flag it for further checks. Thanks to these capabilities, digital payment methods can become more secure, as fraud can be detected in real-time, rather than after something suspicious has already been done. On the other hand, it seems that people’s privacy may be violated due to this technology, as such systems can create enhanced behavioural profiles and thus serious privacy-related questions will arise.

AI and data usage
One of the most notable advantages of AI in financial services is personalisation. The bank application can analyse individuals’ income, behaviour in terms of spending and repayment history in order to provide meaningful budgeting advice. This way, the lending company can improve its assessments of credit risks. Moreover, investment platforms may leverage this technology to deliver timely data to customers, based on their preferences. However, this works the other way as well: in order to deliver better personalised services, customers’ profile should be a well-understood one. As an example, consumers would surely appreciate receiving notifications on their excessive spending or subscriptions. However, it is questionable whether a customer feels comfortable with the way the data is processed and the reasons for sending the specific messages to consumers.

Rise of financial profiling with AI technology
Beyond just identifying events as they happen, AI systems also try to anticipate future events. The model is able to spot signs of potential financial hardship, measure anticipated behaviours with respect to repayment or detect changes in spending habits. Consequently, companies could proactively intervene and make corrections when necessary. For example, a person experiencing cash-flow difficulties may be given information on repayment prior to missing an instalment. Predictive profiling, meanwhile, raises questions about fairness and transparency. If the AI labels a person with a certain risk profile, the person should know what that means for important financial decisions.

The importance of data minimisation
The rise of AI does not mean financial companies should collect all the data they can get their hands on. On the contrary, data minimisation could be a key principle of ethical financial AI. Companies should have a specific purpose in mind when collecting data and only collect what is needed. Lastly, more transparency for consumers about how their data is used is required. Informative notices that are long and difficult to read do not create consent. Consumers need to be aware of what information is collected and how it will be used.

Inference is a key part of privacy

One of the significant shifts due to advancements in AI is that the whole concept of privacy has shifted to the form where it does not only pertain to the facts that a person consciously reveals or gives out. AI is an entity that can utilise any information in order to make conclusions if it is being given some standard ordinary data. An analysis of many transactions can signal a change in the lifestyle of a particular individual. Regular payments can hint at a membership or a preference. Spending items can serve as proof of being under financial stress. Payments made by the person in a certain place can indicate movement. Everything mentioned above may not have been identified by the consumer, but still this thought can derive conclusions from the existing data, meaning that the future financial privacy models will not only look into the security of raw data but also the ideas and expectations received from their use. 

Creating trust in financial AI

If AI is to be used in digital finance successfully, it must be not only technologically viable. The institution operating in finance must have additional protections regarding data access and encryption, model management, and cooperating with third parties. AI systems targeted at serious financial decisions must also be monitored and checked for possible errors and discriminatory outcomes. To consumers, keeping standard digital hygiene is important because examining app permissions, setting up secure identification, avoiding excessive financial apps, and knowing which tools are allowed to use financial data can help them remain safe in cyberspace.

In the end, it should not be to avoid the use of financial data. The responsible use of data can make the payment process safer, make credit more available, and make money management smarter.