Table 3.7. Authorities with responsibilities for investigating ML
Authority Human resources Predicate ML
investigated Investigative powers Indonesian
National Police (INP)
All investigators authorised to investigate ML. The Sub-Directorate of ML has 60 ML specialist investigators. However, all its 6883 personnel have the authority to investigate into ML of which 375 have ML investigation experience.
General offences (excluding customs, taxation, and capital market offences)
General powers under the CPC.
Special investigative powers such as undercover operations, communication interceptions, wiretapping, accessing computer systems and controlled delivery.
Corruption Eradication Commission (KPK)
Directorate of enforcement and execution has 218 investigators (who have the authority to conduct ML investigation of which 114 who have ML
investigation experience. No specialised ML directorate.
Corruption offences General powers under the CPC.
Special investigative powers such a wiretapping and communication interceptions.
National Narcotics
Board (BNN) Specific directorate for ML which has 49 However, there are 883 personnel who have the authority to conduct ML investigation of which 532 who have ML investigation experience.
Narcotic offences General powers under the CPC.
Special investigative powers such as undercover operations, communication interceptions, wiretapping, accessing computer systems and controlled delivery.
Attorney- General’s Office (AGO)
The Sub-directorate of Corruption and ML has 96 investigators (both predicate & ML). However, there are 2841 personnel who have the authority to conduct ML investigation of which 1325 who have ML investigation experience.
Corruption predicate offences investigated by
AGO
General powers under the CPC.
DG of Customs &
Excise Direktorat (DGCE)
All 1362 personnel have the authority to investigate ML of which 640 have ML investigation experience.
333 investigators are specialists in Tactical and Analyst Intelligence. No specialised ML directorate.
Customs and excise
offences General powers under the CPC.
Special investigative powers such as controlled delivery.
DG of Taxes
(DGT) 692 tax investigators in the Directorate of Law Enforcement of which 346 who have ML investigation experience and 134 are digital forensics specialists.
Tax offences General powers under the CPC and the Tax Code
Ministry of Environment and Forestry (KLHK)6
There are 251 personnel who have the authority to conduct ML investigation of which 213 who have ML investigation experience. 45 personnel are attached to the Science and Technology Based Intelligence Centre which supports ML.
Forestry and related
environmental offences General powers under the CPC.
6 A 2021 Constitutional Court ruling interpreted the meaning of money laundering investigator in Art. 74 AML Law broadly to include Civil Servant Investigators with powers akin to the INP which allows investigators of environmental crime, for example at KLHK, to also investigate money laundering.
166. LEA investigators have a reasonably robust process for the identification, selection, and training and continual professional development for both predicate and ML.
The LEAs and competent authorities effectively utilise financial intelligence received from PPATK (FIU) as well as from a variety of sources (see IO.6) to initiate and investigate predicate offences and pursue asset recovery, but to a lesser degree to support ML investigation and prosecution. Although the training syllabi are generally sound and sufficient, these would benefit from continuous update to reflect emerging technology (such as the misuse of social media and virtual assets in criminality). LEAs have collaborated with PPATK on training relating to the implementation of virtual assets and related emerging technology in ML in recent years and such collaboration should continue. LEAs are aware of emerging ML risks but would benefit from further training and resources to be able to effectively deal with launderers employing emerging technology to hide evidence or move their illicit assets. Based on discussions with the LEAs and the prosecutors at the on-site, they were able to show a good ability to deal with ML aspects of the cases, especially with the support of PPATK. However, it remains that most ML investigations conducted in Indonesia relate to self-laundering (see Table 3.10) There are some challenges with obtaining convictions for ML relating to corruption and narcotics as compared to other offences, but these did not arise from significant gaps in the training and expertise of the prosecution. On the other hand, the data shows a very high conviction rate for ML arising out of fraud, embezzlement and cybercrime, which suggests that Indonesian investigators and prosecutors are able to handle financial evidence resulting out of a money trail.
167. In many instances, ML investigations commence at a later stage in the investigation of the predicate offence (see Cengkareng case below), sometimes only after a conviction is achieved in the predicate offence investigation, in order to support asset recovery efforts, which means that ML investigations are not regularly conducted in parallel to the predicate offence. During the on-site, LEAs across agencies communicated to assessors that ML investigation is used to “make perpetrators as poor as possible” rather than to pursue ML prosecutions in itself.
However, initiating ML investigation at a later stage could have a negative effect on asset tracing due to dissipation (see IO.8).
168. In 2017, a specific note was circulated to all branch heads of the AGO with operational policy requirement for any of the above authorities to initiate an ML investigation for predicate offence investigations. The note mentioned above communicated the instruction to pursue ML charges for where elements of ML are found in cases involving narcotics. Based on the data in Table 3.8 on ML investigations by crime type, this appears to have resulted in an increase in the number of ML prosecutions for narcotics offences. Similar notes were circulated for tax and customs, but these have not shown similar improvements. The documents illustrate the awareness and appreciation of pursuing ML investigations, but this has not translated into regular parallel ML investigations. There is also no data to show that these have resulted in more complex ML investigations as well as third party/stand-alone ML investigations.
Box 3.4. Cengkareng (2015-2021) - ML investigation to support predicate investigation and asset recovery
In 2015, a suspicious financial transaction obtained by regional investigators in collaboration with PPATK triggered an ML investigation.
An investigation was conducted into a case of document forgery and bribery of government administrators, which had resulted in State loss of IDR 629 billion (EUR 41.6 million). The offender had placed the proceeds of crime into one account and thereafter moved the money to several different accounts, exchanged some of the funds into foreign currency, transferred assets to family members and purchased assets overseas. In 2021, INP initiated an ML investigation and obtained information from PPATK, KPK, MLHR and the population registry to trace and seize the assets amounting to IDR 700 billion (EUR 46.3 million). INP had to conduct company profiling and analysis of financial transactions as well as documents obtained from overseas to show the illicit origin of the transfers to third parties. The investigation is ongoing.
169. The case studies presented indicated strong inter-agency cooperation on information and intelligence sharing, to support each other’s investigations, both informally and through formal arrangements such as MOUs. The AGO receives ML briefs from predicate crimes submitted by investigators from INP, BNN, DG Tax, etc, and provides legal advice at the investigation stage. On request, KPK regularly shares information in its database collected through compulsory asset declarations of public officials, with other LEAs to support their ML and asset recovery investigations. ML is investigated both by the home office in Jakarta as well as the regional offices of the INP. The home office in Jakarta will handle an ML investigation involving more than one region, ML conducted abroad, ML involving a PEP, or a large ML transaction. INP reported challenges relating to synchronising data and information between the regional offices and the Jakarta home office when handing ML, which is being addressed by greater use of online applications, developing case management systems as well as developing relationships through liaison officers, conducting real time communication as well as regional training opportunities.
170. Except for INP, AGO and BNN, the other LEAs do not have a specialised department or investigators that focus only on the identification of, and investigation of ML. As such, the number of specialist and experienced ML investigators in each agency is low in light of the risk and context of the country. For example, considering the risk relating to corruption, it would be expected that KPK would have greater ML investigation specialisation among its personnel. KLHK has recently set up a team to handle both predicate and ML. Sophisticated ML investigations require specialised support, such as by forensic accountants and forensic examiners of digital devices. This is currently sought from PPATK on an ad hoc basis.
Investigations across agencies, would benefit from direct and regular access to such expertise, within their own departments that is, tailored to their own needs and typologies. This would encourage more sophisticated ML investigation, especially in the pursuit of ML activities of organised criminal groups and foreign predicate offences and initiate more stand-alone ML investigations.
171. The LEAs have broad investigative powers (see Table 3.7) to investigate both predicate offences and ML but more agencies could benefit from access to special investigation techniques such as to carry out surveillance, undercover operations, and utilisation of wiretaps in order to support Indonesia’s information sharing mechanisms, and conduct more complex ML investigations, particularly those relating to third party/standalone ML and those involving the sophisticated use of legal persons. DG Tax has limited arrest and detention powers but may seek the assistance of INP where needed for their ML investigations. DG Tax has made 411 such requests over the period of 2017 and 2022 out of which 105 requests have been acceded to. In the remaining cases, persons whose arrest was sought, voluntarily surrendered to the DG Tax investigator and the request for assistance was withdrawn.
Consistency of ML investigations and prosecutions with threats and risk profile, and national AML policies
172. The 2021 NRA lists corruption and narcotics offences as the highest risk predicate offences for domestic ML in Indonesia, followed by tax offences, banking and fraud as well as forestry and environmental crime. ML is perpetrated by both individuals and corporations, especially limited liability companies. ML often takes place through the use of third parties, including family members and the use of false identities. Money is laundered through banks, real estate, vehicles as well as laundered abroad, which is reflected in the case studies presented by Indonesia during the on-site.
173. Although ML investigations and prosecutions have significantly increased over the last five years, overall, the numbers, as reflected in Table 3.8, is still small in the context of Indonesia.
Table 3.8. Money-Laundering investigations – by crime type
Crime type 2017 2018 2019 2020 2021 2022 (to April) Total (%) ML Investigations
corruption 32 31 39 53 38 20 213 (26.2%)
narcotics 19 50 64 38 37 19 227 (28.3)
fraud/embezzlement/ cyber crime 26 31 41 54 66 12 230 (28.7%)
forestry & environmental crime 1 1 1 3 4 3 13 (1.62%)
customs & excise crimes 1 1 1 1 3 1 8 (1%)
tax crime 0 1 9 2 4 1 17 (2.12%)
others 3 12 19 65 25 3 92 (11.5%)
Total 82 127 174 216 181 59 800
ML Prosecutions
corruption 34 16 18 21 40 12 141 (24.06%)
narcotics 14 42 59 44 50 0 209 (35.67%)
fraud/embezzlement/ cyber crime 0 14 28 49 50 4 145 (24.74%)
forestry & environmental crime 1 1 0 0 2 0 4 (0.68%)
customs & excise crimes 2 0 0 4 1 0 7 (1.19%)
tax crime 1 1 4 2 1 0 9 (1.54%)
others 0 12 11 24 21 3 71 (12.12%)
Total 52 86 120 144 165 19 586
ML Convictions
corruption 13 10 7 25 11 7 73 (17.1%)
narcotics 25 11 20 42 38 9 145 (34%)
fraud/embezzlement/ cyber crime 12 6 25 41 50 9 143 (33.5%)
forestry & environmental crime 1 0 0 0 1 0 2 (0.47%)
customs & excise crimes 1 0 0 0 0 1 2 (0.47%)
tax crime 1 1 1 3 0 2 8 (1.9%)
others 19 6 2 9 14 4 54 (12.65%)
Total 72 34 55 120 114 32 427
Corruption
174. Corruption is investigated in Indonesia by KPK, INP and AGO. The KPK is a highly specialised agency that reports directly to the President of Indonesia and has a broader mandate of eradicating corruption in Indonesia. KPK investigates and prosecutes corruption-related ML cases involving a State loss above IDR 1 billion (EUR 66 146), cases with high public impact, and/or high-level State officials.
Corruption-related cases outside of KPK’s jurisdiction are handled by the INP or AGO. INP and AGO are required to inform KPK (via a Commencement of Investigation Command or SPDP) within 14 days of starting a preliminary corruption investigation. KPK has its own prosecutors and thus conducts prosecutions independent of the AGO whereas INP and AGO prosecutions are conducted by AGO prosecutors.
175. Based on the statistics, the proportion of ML investigation, prosecution and convictions in relation to corruption is not fully in line with Indonesia’s risks. Most of the ML investigations are conducted by INP and AGO with the KPK pursuing only a handful of ML cases. The data reflects that since 2017, KPK investigated only 31 ML cases (out of the 177 corruption investigations) and managed to get only 13 convictions (out of the 72 corruption convictions). The KPK has access to
sophisticated financial intelligence (both from PPATK and through its own sources) and investigative tools and utilises these effectively in its anti-corruption investigations and to pursue criminal assets (see IO.8), that could also be used to better pursue the ML aspect of the offences. During the on-site, KPK communicated to AT that while it prioritises investigation into the corruption offence, it would pursue ML investigations where ML investigation would maximise asset tracing and asset recovery. Considering the profile of corruption investigation conducted by KPK as well as the resources available to KPK, there is scope for KPK to use its expertise and resources to pursue more ML investigations. In light of the nature of cases it investigates that involve substantial amounts of illicit proceeds and complex forms of laundering of the proceeds, there is scope for KPK to pursue complex third party and stand-alone ML cases as well as ML involving legal persons.
Table 3.9. Money-Laundering investigations and convictions (KPK)
ML 2017 2018 2019 2020 2021 2022 (to June) Total
Investigations 7 6 5 3 6 4 31
Convictions 1 2 1 1 8 0 13
Box 3.5. Jiwasraya (2020) - ML involving corruption/embezzlement
The investigation was triggered by a public complaint which revealed that Jiwasraya, a State-owned enterprise, was used by HT and BT to embezzle a total of IDR 16.8 trillion (EUR 1.1 billion). Layering and the disguising of the funds was done using other companies and accounts as well as through the purchase of vehicles in the names of the offenders, third parties and other companies. AGO worked with PPATK for financial information, DG Tax for information relating to the offenders’ and the companies’ wealth and assets, MLHR for company information and the Capital Market Authority for information on the ownership of shares and mutual funds. (see IO.8 for asset recovery efforts)HT and BT were convicted of both corruption and ML. They were sentenced to life imprisonment and fined between IDR 6 and 10 billion (EUR 40 000 and 66 000). Five accomplices were convicted of corruption and sentenced to between 18-and 20-years’ imprisonment and fines.
Narcotics
176. The proportion of ML investigation, prosecution and convictions in relation to narcotics offences is broadly in line with its risks. BNN recognises the importance of financial investigations to uncover narcotics networks and syndicates as well as to pursue the ML offence. BNN has a specific directorate that investigates ML cases, which are triggered by the investigation of the predicate offence as well as information from PPATK. Further, there are regulations that guide the pre- investigation and investigation of ML offences related to narcotics. The regulation also requires BNN investigators conducting ML investigations to prepare reports the results of their investigations for internal record. However, BNN could make more use of financial intelligence from PPATK to identify ML (see IO.6).
177. ML investigations are highly resource intensive and as noted by other LEAs, ML is prioritised where it will support asset recovery. From the point of view of BNN, the value of investigating ML is also affected by the severe penalties imposed for drug predicate offences in Indonesia, including long prison sentences and the death penalty, which may surpass penalties available for ML offence. BNN should nevertheless optimise the use of ML investigations, particularly to enhance the pursuit of organised narcotics syndicates that require complex and stand-alone ML investigations and to track down evidence across borders.
Box 3.6. Erriq Levianto (2021) - ML involving narcotics
Levianto was involved in a drug smuggling syndicate that smuggled drugs from Malaysia to Aceh and then to other parts of Indonesia. The vessel smuggling drugs was intercepted by Indonesia authorities and Levianto was investigated for drug smuggling. However, as the drugs were paid for in cash, there was insufficient evidence to link Levianto to the predicate offence. Based on information obtained from PPATK (see also IO.6), the land registry and population registry, INP was able to show that the assets of Levianto were not consistent with his employment profile.
Levianto was convicted of ML, sentenced to eight years and six months imprisonment and his assets were confiscated.
Other crimes
178. The third highest risk predicate offence for ML in the 2021 NRA is fraud and economic crime and the statistics reflect that proportion of ML investigation, prosecution and convictions in relation to these crimes is broadly in line with the risks. However, in relation to other predicate offences, such as forestry and environmental crimes, customs and excise as well as tax offences, that also pose significant ML risks, the number of ML investigations, prosecutions, and convictions is relatively small. Indonesia has noted that tax investigators have limited capacity to handle ML investigations due to the complex structure of such investigations and this can be improved through better resourcing. Although the ML risk relating to environmental crime (particularly forestry) was recently reclassified as medium risk, the KLHK confirmed during the on-site that ML relating to forestry still results in very high levels of illicit proceeds that are laundered in a sophisticated manner.
As such, these are the areas that require significant focus from the Indonesian authorities. Although the relevant case studies showed significant confiscations, they did not illustrate that complex ML structures were being regularly and effectively uncovered. KLHK authorities are well aware of the high volume of criminal proceeds that are laundered through forestry and environmental crimes and recent structural developments within KLHK to enhance parallel ML investigation and prosecution is encouraging and should be supported with appropriate training and resourcing as well as through the adoption of robust internal policies, mechanisms and procedures.
Box 3.7. RPS (2018-2021) - ML involving illegal logging
INP commenced pre-investigations based on public complaints and found that illegal logging had taken place. RPS had logged beyond the allowance provided by the forest concession. INP’s investigation, coordinated with PPATK, revealed that IDR 6 billion (EUR 400 000) had been laundered through the company owned by RPS. While other participants were convicted for illegal logging, RPS was convicted for illegal logging and ML, and sentenced to 1 year and 6 months imprisonment and fined IDR 500 million (EUR 33 000). Assets amounting to IDR 6 billion (EUR 400 000) were identified for confiscation. The company was investigated and charged for corruption and ML. The company was fined IDR 7.5 billion (EUR 500 000).
Types of ML cases pursued
179. The statistics (see table 3.10) and case studies reflect that LEAs focus on self- laundering offences and to some extent on third party ML, with only a very small number relating to foreign predicate offences and stand-alone ML. This is despite the fact that Indonesia has tools to pursue ML for concealing or disguising the origin of funds (Art. 4 of the AML Law) on the basis of information of business registration in an official government database, reported tax or legal documentation relating to the business licenses. However, as noted above, generally, investigation into ML only takes place on the back of an and trailing behind the investigation into the predicate offence. This negatively impacts the ability to identify ML, independent of the conduct of the predicate offence. Thus, despite the fact that case studies illustrate Indonesian authorities’ ability to conduct complex ML investigations, the statistics reflect that in practice, this is not being sufficiently conducted outside self- laundering cases. For example, although financial investigation is regularly conducted to trace assets that have been transferred to third parties, these third parties who assist with the laundering of the assets are not always prosecuted for ML.
Table 3.10. Types of Money-Laundering Convictions
ML 2017 2018 2019 2020 2021 2022
(to April) Total (%)
Self-laundering 57 16 37 105 99 30 343 (80.5%)
Third party laundering 14 12 18 13 11 0 68 (16%)
Foreign predicate offence 0 5 0 2 2 2 11 (2.6%)
Stand-alone ML offence 1 1 0 0 2 0 4 (0.9%)
Total 72 34 55 120 114 32 426