By John Spooner, Head of Artificial Intelligence, EMEA, H2O.ai
Artificial Intelligence (AI) has evolved significantly from being a mere technology buzzword, to the commercial reality it is today. The technology is making a positive impact across many industries, including the financial sector. The financial services industry has a reputation of constantly innovating and advancing new technologies, in the pursuit of strengthening the customer base, and finding new revenue opportunities. This is happening across all segments including capital markets, commercial banking, consumer finance and insurance.
The use of AI in the financial services is rapidly changing the business landscape, even in traditionally conservative areas. According to a recent Bank of England survey of 500 UK financial institutions, two third
s of respondents were reported to have already been using machine learning in some form, with the median firm using live ML applications in two business areas. This is expected to more than double within the next three years. Financial institutions today utilise AI for areas such as customer service, risk management, fraud detection and anti-money laundering, while adhering to regulatory compliance.
AI technology has proven to be reliable, especially when it comes to detecting money laundering, and is empowering leading financial services to tackle such issues in an increasingly effective manner.
Money laundering is defined as “the concealment of the origins of illegally obtained money, typically by means of transfers involving foreign banks or legitimate businesses.” Reuters reported in 2017 that the total US and EU fines on banks’ misconduct, including anti-money laundering violations since 2009 amounts to $342 billion.
Money laundering poses a serious threat to the financial services sector. According to the United Nations Office on Drugs and Crime, an estimated $2 trillion is “cleaned” through the banking system every year. Fines for banks that fail to prevent money laundering have increased by 500 fold in the past decade, and is now worth more than $10 billion per year. As a result, banks have constructed large teams, and allocated them the time-consuming tasks of identifying and investigating any suspicious transactions, which often takes the form of multiple small transfers within a complex network of players.
Traditional Approaches for Tackling Money Laundering
Typically, investigation teams use rule-based systems like FICO, Fiserv, SAS AML or Actimize to identify any suspicious transactions. This rule-based workflow consists of the following three steps: Firstly, an alert is generated by the alerting system; secondly, the investigator reviews it using information from different sources and finally, the alert is approved as True Positive or classified as False Positive. A False Positive can be defined as an error in data reporting, in which a test result improperly indicates the presence of a condition that in reality is not present.
However, the problem with rule-based systems is that they create a large number of false positives, usually in the range of 75 to 99 percent. These means that a vast amount of time and manual effort is being wasted to investigate these false alerts. The high number occurs because the rules can become outdated quickly and it take time for the systems to be recoded.
How AI Can Address False Positives
Anti-Money Laundering (AML) programmes that are used in capital markets and retail banking extensively deploy rule-based transaction monitoring systems, spanning areas across monetary thresholds and money laundering patterns. However, bad actors can adapt to these rules over time, and tweak their methods accordingly to avoid detection. This is where AI-based behavioural modelling and customer segmentation can be more effective, in discovering transaction behaviours and identify behavioural patterns and outliers, that indicates any potential laundering.
AI, especially time series modelling, is particularly effective at examining a series of complex transactions and finding anomalies. Anti-money laundering using machine learning techniques are able to identify suspicious transactions, and also irregular networks of transactions. These transactions are flagged for investigation, and can be scored as high, medium, or low priority, so that the investigator is able prioritise their efforts. As the actors modify their behaviour, so does the AI that is underpinning the programmes, meaning the number of false positives stays low while maintaining a high number of true positives.
AI can also provide reason codes for the decision to flag transactions. These reason codes tell the investigator where they might need to search to uncover the issues, and help to streamline the investigative process. AI is also able to learn from the investigators throughout the review, clearing any suspicious transactions and automatically reinforcing the AI model’s understanding and ability to avoid patterns that don’t lead to laundered money.
AI vs Rule-based Systems
AI-powered AML systems provide many advantages over an existing rule-based system. This includes being able to dramatically reduce false positives, provide a curated set of alerts to the investigator and the ability to ingest domain specific IP customised for money laundering. The AI technology can be strategically placed between the AML rule-based system and the investigator, which allows companies to gain a rapid return of investment. Overall, the average investigation time is dramatically reduced from between 45 to 90 days, to mere seconds. It also greatly reduces any human inaccuracies and hours required per person, and can fit rule-gaps with innovative features.
Address Money Laundering and Drive Productivity
When used effectively, Artificial Intelligence (AI) can be a critical factor to success in the financial services industry. It enables financial services companies to not only efficiently build personalised banking experiences, fraud and money laundering models but will also improve employee and business productivity. As money laundering networks become ever more complex, the time is now, for progressive financial intuitions to start embracing AI in order to effectively combat money laundering, and to focus even more effectively on driving overall productivity.
Accounting Automation in the Future
Accounting automation is the process of streamlining repetitive tasks in financial processes. For example, some processes like invoicing are time-consuming and repetitive. Automation can reduce manual labor and save businesses both time and money. Also, it helps improve accuracy, reduces errors, and provides more accurate financial reporting.
Accounting automation in the future will be increasingly important for businesses to stay competitive. But every new change comes with both advantages and challenges. Let’s dive in to get ready for this future trend.
Potential Future Benefits of Accounting Automation
Increased Efficiency and Cost Savings
Accounting automation is a great way to increase efficiency and cost savings. For example, AI bookkeeping uses advanced algorithms to automate many accounting tasks. So, companies can track expenses, prepare financial reports, and more using AI.
It reduces the time needed for manual entry. So, businesses can spend fewer labor hours on tedious processes. They can increase efficiency by freeing up resources for more strategic work. It also helps reduce errors and inconsistencies associated with manual processes. So, the cost of compliance is lower because of greater accuracy.
Improved Accuracy and Reliability
Accounting automation can improve accuracy and reliability in accounting processes. For example, Automating bank reconciliation is less prone to errors from human mistakes or miscalculations. You can automate the process to identify discrepancies between the bank statement and accounting records. It helps to ensure that financial reports remain accurate and reliable. So businesses can take corrective action faster than processing data manually.
Streamlined Business Processes
Streamlined business processes involve eliminating unnecessary steps, reducing paperwork, and automating repetitive tasks. This allows businesses to focus on higher-value activities, such as developing new products, improving customer service, and developing strategic plans for the future.
Making a Better Decision
Accounting automation can enhance decision-making in 3 ways.
1. It enables businesses to access real-time information from multiple systems. So they can identify trends for better decision-making.
2. Automated accounting also helps with forecasting, budgeting, and auditing tasks. It enables businesses to be more proactive in their decision-making processes.
3. Also, automated accounting tools can integrate with enterprise resource planning (ERP) systems. They can manage data across the enterprise and make concise decisions that are favorable to the company as a whole.
Increase Customer Satisfaction
Accounting automation can help businesses increase customer satisfaction by streamlining their processes and providing a more efficient customer experience. For example:
4. Automated accounting systems can automate tedious manual tasks such as invoicing, data entry, and payroll processing. This allows businesses to focus on other aspects of their operations that are more important for customer service.
5. Automated accounting systems can also provide customers with more accurate and timely financial information. The information can help them make better decisions about their finances.
6. Also, accounting automation enables businesses to respond quickly to customer inquiries. It helps reduce wait times and improve the overall customer experience. So, you can build better relationships with their customers.
Accounting automation takes place online or comes with cloud-based solutions. So, you can access your information and do your job from anywhere instead of being confined to one spot.
Challenges to Implementing Accounting Automation in the Future
Cost of Technology Infrastructure Upgrades
Automating an accounting system often requires businesses to invest in new hardware and software, such as servers and other associated equipment. These upgrades come with a hefty price tag that may be difficult for small businesses to afford.
There are also extra costs, such as installation fees, setup charges, software licensing fees, cloud storage costs, and maintenance fees.
Training Requirements for Staff Members
Accounting automation involves using advanced technology to automate certain processes. So, it creates a need for trained staff members who can handle the new technology. Training requirements vary depending on the type of software used.
Some common training includes record-keeping procedures, software applications, and troubleshooting skills.
Regulatory Compliance Issues
Accounting automation can be a time-saver, but it also requires firms to be aware of the applicable rules and regulations. Companies must ensure that their automated systems are compliant with relevant laws and regulations such as Generally Accepted Accounting Principles (GAAP), International Financial Reporting Standards (IFRS), and other applicable accounting standards.
Besides, they must also comply with legal requirements related to taxes, financial statements, and other reporting obligations.
So, businesses must consider the complexities of regulatory compliance when automating accounting.
Security and Data Protection Concerns
As businesses move their accounting processes to the cloud, they are exposed to a wide range of potential security risks. Data breaches can cause significant damage to the business’s financial and reputational integrity. Besides, the complexity of automated accounting systems can make it difficult to identify and detect suspicious activities or errors in the system.
To ensure data is kept secure, businesses must have strong measures in place to protect against unauthorized access, encryption, and regular backups of data.
Furthermore, companies must train their staff on the proper use of the system. It helps staff to know how to protect confidential information from being accessed or misused by unauthorized personnel.
Businesses may also need an experienced IT team to monitor and maintain the system to keep up with any changes or updates for optimal performance.
Accounting automation has come a long way in the past few decades. It is likely to continue to advance in the future. As technology continues to evolve, more businesses will likely begin taking advantage of automation in their accounting processes. So, businesses should be aware of the potential challenges and prepare to stay competitive.
Author bio: Kassidy Li is a Certified Public Accountant and online entrepreneur who is passionate about helping people to solve problems and grow wealth with accounting knowledge and technology. She has 10+ accounting experience in small to large-scared corporations and expertise in financial accounting, management accounting, budgeting, and payroll.
Three ways data can help financial organisations thrive in today’s economy
By Rinesh Patel, Global Head of Financial Services, Snowflake
Financial organisations are caught in the middle of an ever-evolving landscape caused, in part, by emergent fintechs, shifting consumer expectations and increased regulatory change. Businesses are therefore turning to their data, re-imagining how they collect, process and analyse it, to drive growth and opportunity.
Despite this intention though, firms can often find themselves overwhelmed with the amount of data at their fingertips. Data tends to reside in individual departments that have no secure, efficient way of sharing it with other teams, creating silos of information. When teams need to collaborate, organisations are faced with additional costs and complexities in the movement of that data. The current infrastructure used by many financial institutions is not able to support the changing requirements of the industry, where data is the lifeblood.
Firms looking to harness their data should leave behind their outdated legacy architecture and implement an enterprise data strategy with a cloud-native platform. They can reposition themselves to accelerate time to market and value, with differentiated products and improved client offerings to gain a critical competitive advantage. Here are three ways that financial services are using better technology and enhanced data management to add business value.
Adhering to regulatory requirements
The volume of global regulations and reporting obligations has risen exponentially in the past decade, creating greater complexity and security challenges for firms capturing and processing data. Many of these regulations were taken by supervisors to ensure financial stability after the financial crisis of 2008. Regulators have greater expectations of firms with the aim of risk mitigation and transparency. With advanced technologies facilitating data capture, storage and analysis now available, supervisory bodies are also keen in part, to ask for additional disclosures because it’s now possible to demand more documentation and seek greater transparency.
The landscape of differing interpretations, overlapping regulatory requirements across asset classes and geographies and strict, even unrealistic deadlines for implementation have forced customers to take tactical quick-fix solutions, elevating operational risk and the chance of regulatory fines. Compliance departments have therefore been spending years building reporting processes, managing inconsistent data sets, maintaining ageing data stores and importantly overseeing differing levels of governance, adding more cost and complexity to the task at hand. For a large multi-segment global bank or asset manager this fragmented and manual approach to data management and analysis is not sustainable given the scale of processes and multi-geographic considerations that they have to comply with.
As regulators continue to push the long-term structural change agenda, financial services must now ready themselves to meet more robust reporting requirements to comply with the ever-changing regulatory landscape. The objective is to simplify and better manage data across teams with the governance and security provided by technological capabilities now offered through modern cloud capabilities to drive needed reporting. This will allow firms to replace old and inconsistent data with a centralised data architecture, providing a single source of truth. The time and cost reduction from data sourcing, ingestion, and the normalisation of data for analysis, can shrink to significantly streamline reporting processes.
Customer 360 experience
Consumers provide financial institutions with a vast amount of information, ranging from their banking habits to their behavioural preferences. Financial organisations have traditionally been slow to tap into the totality of this information to provide a better experience for customers.
The quest to provide greater visibility and a 360-degree view of customer behaviour is at the core of financial services organisations’ priorities. Customers want smooth, easy digital experiences that can speak to their desire for ease of use and convenience. This is seen in the ways virtual banking consumers have opted for technologies that are simple to interact with, self-directed and frictionless when it comes to carrying out digital transactions. New regulations, such as PSD2 and rules around open banking have also primed customers to expect more.
The challenge for legacy institutions is to bring the ease and usability of digital-first platforms with the sophistication of a major, global provider. Tapping into the full spectrum of data created by consumers is central to a successful transition.
Wealth advisory, investment management professionals are increasingly looking at data capabilities to support ongoing relationship management with their clients. Using data to understand customers in this way helps banks to successfully move customers up the wealth value chain. Wealth management organisations can digitise the investment process – from finding customers to managing accounts, and offering bespoke plans. Effective use of data in this sector can free up time for advisors, helping to retain key customers and charge higher commission levels thanks to a new level of personalised service.
Developing an effective ESG strategy
Environmental, social and corporate governance (ESG) considerations have grown in significance with increasing stakeholder pressures, driving a response by firms to prioritise their sustainability agenda. To understand, evaluate the problem and take action, firms need access to technology providing holistic ESG data capabilities and solutions, with performance and scale.
Financial firms are amassing large data sets from the public sector, including government reports, scientific bodies and private sector reports, to understand and address the climate challenge. Businesses are moving with urgency to acquire robust data sets, to meet ESG criteria and sustainability metrics needed to evaluate impact and make progress against their own commitments. There are several pervasive business use cases for teams experiencing ESG data challenges, including portfolio construction, financial planning and regulatory reporting that will require an effective ESG data management strategy.
Ever present challenges in the ingestion, standardisation, and sharing of ESG data will be at the forefront of every organisation – as they process the magnitude of the challenge and transform their operations to address the issue. With cloud-native solutions, firms can use ready-to-use query data across established marketplace data sets. They can then share that data across teams in a secure, governed way – with greater speed to market. Organisations can meet the need for scalable analytics, and access a data ecosystem to build their own proprietary ESG applications for different user and workflow requirements.
A business fit for the future
With data cloud solutions, businesses can effectively analyse the vast amounts of data available to them, equipping them to meet the ever-changing financial landscape. Leaving behind legacy systems will open up a multitude of opportunities and benefits that will drive business growth. This includes developing a 360 view of the customer, improved data governance and the opportunity to use data to support an effective ESG strategy. Without the ability to harness data through the cloud, companies will get left behind the competition and struggle to meet the standards that modern consumers expect.
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