AI for Better Decision-Making
Better decision-making isn’t always about deciding whether A or B is the optimal choice. Sometimes it’s about rethinking what kind of decisions to make. Consider that it may be easier to make one big decision well, rather than hundreds of smaller decisions that each have different payouts and risks.
In contrast, computers are very good at finding patterns in large datasets to automate hundreds of smaller decisions. For example, buying stock in one company involves determining which stock will provide the best results, balanced with risk. However, this may not yield the best cumulative outcome over time. Making hundreds or thousands – or even of millions – of smaller trade decisions can generate far more profit with less risk.
Converge Technology Solutions helps its client generate real value from data by building custom AI solutions with Dell infrastructure. These systems allow companies to make new kinds of decisions to improve their profitability. Rather than focus on making a single good decision, companies can decide between a myriad of targeted opportunities that provide greater return on investment.
Converge also uses AI to accelerate how fast companies can leverage this value. For example, Converge worked with an international bank to create AI-driven search incorporating natural language processing to help staff find resources they need easier and faster.
Faster Decisions with High-Frequency AI
One of the most important edges in trade transactions is the ability to make lightning-fast decisions. Today’s global hedge funds and others focused on high-frequency and algorithmic trading need to be able to identify opportunities and act on them before the competition. These organizations run massive AI-driven simulations to forecast market conditions and make money when the market moves in a particular direction. Increasing the challenge is that to make better decisions, these simulations need to include more and more data in their calculations.
Business Systems International (BSI) has been a market leader in information technology solutions for the financial market since the mid-1980s. BSI’s solutions make use of a wide range of Dell Technologies, including accelerated-optimized, high-density Dell PowerEdge servers. In addition to giving financial companies a processing edge, these systems are compact, quickly and easily scale.
Automation through AI
For many companies, their first encounter with AI is to automate a process within their business. Part of the challenge of automation through AI is choosing a specific project, determining exactly what needs to be done, and then scaling AI capabilities as the workload expands and grows.
With its investment in AI, Trintech now has bots perform many of the routine accounting and financial services they provide to their customers. This, in turn, has made it possible for Trintech to support 3X as many software as a service (SaaS) customers for a 300% increase in revenue. In short, with AI technology, Trintech can do more faster, and at a lower cost.
Visualization for New Insights
Many insights can be gained from data when the underlying relationships can be seen and understood. However, resolving questions about relationships in large data sets is not always practical or sometimes not feasible at scale using queries. Graph analytics can be used to analyze thousands of data sources with up to billions of elements to make more rapid and more accurate decisions.
Graph analytics is one of the fastest growing markets in AI. Graph technologies are expected to facilitate rapid contextualization for decision-making in 30% of organizations worldwide by 2023, according to Gartner.
Graph analytics with TigerGraph enable the deep exploration of connected data by tracing the complex interrelationships among various entities, such as organizations, people, transactions and other records. Most importantly, the analytics are in context.
TigerGraph does not require time-consuming specialized programming to create nested queries, table joins, or multiple scans across large tables. Rather, the pre-connectedness of the data supports traverses of 10+ hops, enabling discovery of a whole new range of interrelationships for financial decision-making, including fraud detection and recommendation engines.
With its ongoing evolution, AI will continue to change how financial organizations generate value from data. Through automation, AI simplifies and increases the efficiency of processes and operations. As AI evolves, it will change not just what businesses do but also the very fabric of how they make decisions. Learn more about AI can help generate more value from data at HPC & AI on Wall Street.
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