Financial organisations are only ever one decision away from a completely different outcome, and so must rely on a superior ability to react and allocate their resources according. However, the practicality of this is that too often it can be difficult to determine exactly what the most attractive next step is.
As has been applied vigorously to the financial services industry, artificial intelligence (AI) has stepped in to allow companies to make smarter, better-prepared decisions. In this guest post for The Fintech TImes, Ajay Khanna discusses how the use of AI technology within decision intelligence could enable financial organisations to maximise their business performance and potential.
Khanna is the CEO and founder of Tellius, an AI-driven decision intelligence platform that enables anyone to get faster insights from their data. The company helps organisations accelerate their journey from data to decisions by augmenting human expertise and curiosity with intelligent automation.
The Tellius platform combines AI- and ML-driven automation with a search interface for ad hoc exploration, allowing users to ask questions about their business data, analyse billions of records in seconds, and gain comprehensive, automated insights in a single platform.
Khanna is a tech entrepreneur who has a passion for building disruptive enterprise products with an awesome user experience. Prior to starting Tellius, Khanna was CTO and founding member of Celcite, a fast-growing telecom analytics and solutions company, that was acquired by Amdocs. He has over 25 years of extensive experience working in various technical, business and consulting roles.
Modern financial services institutions thrive off efficiently capturing new opportunities, managing risk, and strengthening customer relationships. Similar to other industries, these organisations can immensely benefit from digital transformation efforts so that they can not only boost efficiencies but also maximise data assets to enhance operations.
Unfortunately, many financial services organisations continue to face a deluge of data and struggle to arm internal departments with modern tools and expertise to use it effectively to their strategic advantage. In fact, a recent study from Workday found 77 per cent of financial respondents rely on labour-intensive manual processes to analyse their data; these organisations aren’t capitalising on all of their data because they don’t have the time or resources to uncover actionable insights from all the data.
On top of that, outdated analytics tools and processes can lead to undiscovered insights and sub-optimal operational decisions which ultimately inhibits organisational growth. For example, financial lenders need to enable business leaders and operations teams with easy ways to assess customer risk at a granular level to minimise the likelihood of bad investments and loan defaults. Traditional analytics tools would create significant lag times for insights as the data would need to be cleaned and analysed by hand.
To combat these challenges, organisations are relying on decision intelligence, an approach that combines AI and machine learning with data analytics, to accelerate a deeper understanding of what is happening in the business, the reasons why metrics change, and specific recommendations on how to impact business outcomes. With decision intelligence, organisations see new levels of data productivity, transparency, and most significantly, a faster turnaround when it comes to getting insights into the hands of business teams that need them to drive decision making.
Here are some of the key ways that financial services organisations can use AI-driven decision intelligence to enhance and maximise business performance.
Managing risk
Managing risk is essential within financial services. To inhibit risk exposure, organisations need to clearly define, understand, and manage key areas like operations, regulatory compliance, supply chain, and credit checks so that they can detect risk in real-time and minimise the likelihood of a bad investment. For example, when reviewing any type of loan application – whether it be for a mortgage, car, or a small business – financial lenders need efficient tools to analyse potential risk before they grant a loan.
AI-powered decision intelligence streamlines the process of unifying transactional, customer, and third-party data and applying machine learning algorithms to identify the factors and behaviours tied to higher risk. Further, it enables business teams to easily access and understand the results of the analysis.
With the entire workflow, from data to insights, housed in a single application – instead of spread across multiple disparate applications that require multiple copies of data to be made – business teams and data experts can then collaborate in the process of discovering meaningful insights from data, thereby iterating much faster. In the end, combinations of risk factors – instead of just solely someone’s credit score – can be uncovered in a much more granular way across all of the data for segments of customers as well as individual customers.
This automated process also improves productivity – alleviating data teams of time-consuming data prep and long lag times – and ultimately results in a better customer experience to qualified customers who are awaiting important loan approvals.
Improving marketing and upselling campaigns
Having real-time access to customer insights not only allows financial institutions to increase personalisation and engagement strategies, but it can also help drive acquisitions and upselling tactics. It’s no surprise that financial organisations are always upselling new products and services to their customers, but as consumers’ personalisation expectations continue to rise, the onus is on the financial services institution to get creative in how they engage consumers.
With decision intelligence, financial institutions can derive insights that deliver the right offering to the right customer in the right channel – which is critical when it comes to successfully upselling and cross-selling. With a holistic view of customer relationships and their unique preferences – merged with historical data – financial institutions can create data-backed marketing campaigns that boost revenue, sales, engagement, and customer relationships.
Identifying fraud
Today, every $1 of fraud loss costs financial services organisations $4. With cybersecurity risks continuing to evolve, fraud is having an ever-growing impact on financial services organisations’ bottom lines.
Unfortunately, many financial organisations rely on legacy monitoring systems that either miss or don’t catch fraudulent transactions altogether. Similar to managing risk, machine learning algorithms can conduct real-time analysis on transactional data – from the largest data sets to user profile behaviours – and detect major areas of fraud such as false declines – one of the largest areas of fraud in financial services. By processing variables like transaction size, location, time, devise, and purchase data, decision intelligence tools can provide real-time judgment on whether a transaction is fraudulent and allow financial institutions to make more informed and accurate decisions.
Not only is this a more proactive approach, but it instils customers’ trust and confidence in financial institutions’ products and services—thereby boosting loyalty. According to Digital Banking Report, banks that invest in digital trends have higher rates of recommendation, greater wallet share, and are more likely to upsell or cross-sell products and services to existing customers.
Financial services is an industry that has been plagued by the bottlenecks of legacy analytics tools and a lack of data transparency. With talent shortages growing, financial teams need robust, modern tools that can not only detect business-impacting loss and missed sales opportunities, but also improve customer experiences. Decision intelligence is the key to unlocking faster, more accurate data insights that can maximise business performance and streamline efficiencies that have for so long limited productivity and results for financial institutions.