Data Analytics has been the backbone of some of the revolutionary companies that are disrupting the ecosystem. Financial Services is no different. These challenges are picked by our editorial team based on the latest trends and development. Do read it till the end to enhance your understanding of the subject. If you think we are missing anything, let us know in the comments and our team will review and add it into the blog.
Neural networks and other machine learning models are quite complex, which makes them harder to understand and explain than traditional models. This naturally leads to some degree of risk and demands an increased level of governance. To avoid fines, remain compliant and minimize the risk of making bad business decisions, banks must be able to explain their models and the rationale behind them – in-depth – to regulators, auditors and internal stakeholders.
It’s obvious that AI technology is data-hungry. AI and ML models can take in vast amounts of big data, and they become more efficient automatically through this experience. This results in greater accuracy and predictability over time. But its important to feed the models with a steady supply of trusted, good-quality information. Lack of good data preparation and specific measures and processes to ensure, data quality is something the banking industry must look into.
A financial organisation must look to move abstract concepts about AI from theory to practice so they can be used in daily operations. The right AI technology can automate labor-intensive manual processes, offer the level of performance needed to make use of the latest technologies, centralize model governance and apply it across all models and Integrate with existing systems and be reusable for other purposes.
Artificial intelligence has brought a new dimension: collecting and grouping all the data available is crucial to ensuring the generation of high-quality AI algorithms. Banks in order to survive in the 21 st century must quickly set up chains for collecting and using data, for enhanced decision-making and to provide a better service for all it’s players – whether it be physical or online trade.
The IT infrastructure of traditional banks need a rehaul. Currently the data centre technologies used by financial organisations (virtual machines, shared drives, etc.) do not perform well on fast data / big data-type technological tools, designed for large-scale standard systems. Therefore by shifting to a cloud-based infrastructure, the organisations in question could overcome these difficulties.
Financial organisations using AI easily be subjected to bias. AI technology feeds on data and we know that there would be some kind of bias in the data itself; most of these biases derive either from minority population being poorly represented in a data set or when human judgment and bias are encoded into the training data itself. Proper modelling techniques, data ethics training to data scientists backed up by ethical and managerial considerations are essential for the optimal functioning of AI.
It’s not enough to adopt AI. Organisations must make sure that the algorithms are reliable for ethical and security reasons. The level of reliability depends on two factors: reliability of the data and the degree of control over the system as a whole and the algorithm process. The slow but steady methods of Test Driven Development which places assessment and verification to develop the required algorithm at its core is needed for reliable system that can withstand the test of time.
Interaction between humans and AI algorithms is a key issue in the fight against financial crime because the final decision is often made by a human analyst and the administrators who handle the alerts.
