Technology is disrupting the financial sector and driving digital transformation
Through James Brier, CTO, Smart delivery solutions
Rapid advances in technology have elevated the financial industry to levels that only science fiction fans could have imagined before. McKinsey estimates that artificial intelligence technologies have the potential to generate up to $1 trillion in added value for the global banking industry each year. Long gone are the days of stockbrokers yelling on Wall Street, replacing the chaotic organic crowd of the trading floor with a few quants and their computers. But don’t be fooled by their Harvard degrees, the real hero is AI and the data quality management techniques that make it so powerful.
A few areas in particular have led to this seismic change: data lakes and cloud computing, data cleansing techniques, AI and machine learning. When this trio of technologies come together, organized in a new world of augmented intelligence, it creates a technological feat that no single human could ever match.
The importance of cloud data and data lakes
If you think of these three technologies as a funnel, leading from the ingestion of data across multiple platforms and channels to their final output in decisions, you can think of data lakes as the initial pooling of data. Data lakes can store “raw” data, without the need to organize it explicitly. Forget big data, we are now faced with endless streams of data, as well as huge costs, duplicate and dirty data, and those huge volumes of data that are constantly changing and moving, as you go. as transactions and decisions are made, and as data changes in real time.
This opens up opportunities for highly efficient IT, with a need for accurate data to collect and use in real time – think dashboards with constantly updated dials and charts as strategies are implemented. at the nanosecond level with proactive control levels not shown.
Data quality and management techniques in finance
As AI is the poster child for technological advancements, making the “last call” on the data fed into it, and data lakes create space for data to accumulate, with technology enabling Ensuring high data quality has become one of the fastest growing areas of investment in banking and finance.
Although often the somewhat forgotten middle child that makes everything possible, data quality is the real unsung hero of the day. Even within the sanitary limits of Swiss banks, so often associated with efficiency and accuracy, “dirty data” still leads to inaccuracies, tainting the bottom line of balance sheets.
If the data fed into your AI algorithms is impure, the output could lead to poor decisions and catastrophic reputational damage if not detected early and at the source.
Choosing a Data Quality Solution Fit for the Modern Era
The world of data quality is only getting exponentially more complex, more regulated, and with less easily definable data parameters. Although thanks to advances in data quality techniques, navigating this often murky world and using it to your advantage has become more accessible than ever.
Profiling tools and forensic techniques included in software such as IDS’ intuitive iData toolkit provide an end-to-end solution integrating everything from ingest, ETL (extract, transform and load) , migration, data obfuscation and synthesis, and test data management, all in one tool.
With a data quality market worth billions growing at a rate of 18% per year, finding a data quality solution that is truly fit for purpose can be difficult, to say the least. can say. Common features of data quality software include the ability to correct structural errors, filter out unwanted outliers, manage missing data, validate data, and provide an indication of data quality assurance. But few tools are capable of securing more than a tiny fraction of the data, and many require huge amounts of manual manipulation as data is pulled from one tool and moved to another – which takes time, is error-prone and may expose the data to the risk of breach.
Our proprietary technology is programmed to continuously scour data for quality flaws before they spiral out of control, providing peace of mind when your organization has a constant flow of data to and from multiple data lakes.
A recent Deloitte the survey showed that 49% of respondents were “very concerned” about “risk data (data that can demonstrate levels of risk), with 69% of respondents indicating that improving the quality, availability and timeliness of risk data is a top priority With such an interest in data quality assurance, those who do not understand how data management techniques can benefit decision-making will quickly find themselves on the roadside.
AI and Machine Learning: The Practical Implementation of Data Quality Techniques in Banking
Few would dispute that we are now in an AI-powered digital era, facilitated by falling data storage and processing costs, and rapid advances in AI technologies. Those who do not embrace AI and make efforts to put it at the heart of their strategy and operations (by adopting an “AI-first” approach), will quickly be overtaken by the competition and abandoned by their clients.
McKinsey Global AI Survey showed that nearly 60% of respondents in the financial services industry had already integrated at least one AI capability. The most used AI technologies were robotic process automation for structured and predictable tasks, virtual assistants or conversational interfaces for customer service divisions; and machine learning techniques for early fraud detection and risk management.
However, none of this would be possible without the use of reliable, high-quality data.
A regular speaker at industry events and an expert in data assurance, James Briers worked with specialist consultancies and delivered major programs for Barclays, HSBC and the NHS before launching IDS and pioneering of iData and the Kovenant™ methodology.