Page 36 - Understandinging Forensic Technology Landscape

Robotic process automation

(RPA), artificial intelligence (AI)

and emerging technologies

RPA, AI and emerging technologies Automation (AA) describes machines that act on their
in the forensic context own volition, such as autonomous trading engines
used on stock exchanges, or robots that perform tasks
Emerging technologies describe a wide range of new unsuitable for humans (for example, bomb disposal).
tools and approaches that are rapidly developing, are
growing in prevalence, and are expected to have a RPA
broad impact. Most fall along the following spectrum of In RPA, software programs (“robots” or “bots”) automate
increasing sophistication and capability: digital tasks previously performed by humans. Original
processes are either mapped or captured through
• Rules-based automation is an automated process observation of a human worker. Software programs then
that performs repetitive tasks in highly structured replicate those processes. RPA is becoming increasingly
software environments that leverage familiarity with sophisticated, sometimes scheduling future tasks,
common programs (for example, macros and scripts). repeating tasks on a recurring basis, or even activating
• Robotic process automation (RPA) is the automation other robots to work in a series.
of labor-intensive, repetitive activities by replicating
human efforts in a software program. Once programmed, robots can work autonomously,
saving time and effort. For example, robotic processes
• Business process orchestration involves combining can write correspondence based on inputs or open and
workflows featuring application programming respond to email. RPA is also useful for pulling together
interfaces (APIs) as well as RPA to model, simulate, information from multiple data sources/systems.
and extend automation across a business process.
Orchestration is often used to re-engineer whole Although advanced RPA can be programmed to perform
business processes or to digitize manual tasks. tasks depending on context, it is not intelligent. That is, it
does not automatically change its performance or “learn”
• Intelligent automation (IA) or intelligent process
automation (IPA) combines RPA with AI and based on its experience. It follows business rules in a
analytical technologies to learn via pattern analysis. process defined by the user, although these rules can
IPA improves over time through either unsupervised include complex decision trees and logic statements.
learning or via guidance through human interaction The best-use cases for RPA typically have the following
(supervised learning). These approaches typically attributes:
perform more advanced tasks than RPA, including • Highly standardized rules to follow
interpretation of text through natural language
processing (NLP), unstructured decision making via • Low complexity
machine learning (ML) and even suggestion engines • High volume of inputs in a consistent or stable
via cognitive agents. environment
• Other advanced approaches include algorithmic • Time-sensitivity of the tasks
models and more advanced AI. Autonomous

Understanding the forensic technology landscape | 32